def K_dominance_check(self, _V_best_d, Q_d):
"""
:param _V_best_d: a list of d-dimension
:param Q_d: a list of d-dimension
:return: True if _V_best_d is prefered to Q_d regarding self.Lambda_inequalities and using Kdominance
other wise it returns False
"""
_d = len(_V_best_d)
prob = LpProblem("Ldominance", LpMinimize)
lambda_variables = LpVariable.dicts("l", range(_d), 0)
for inequ in self.Lambda_ineqalities:
prob += lpSum([inequ[j + 1] * lambda_variables[j] for j in range(0, _d)]) + inequ[0] >= 0
prob += lpSum([lambda_variables[i] * (_V_best_d[i]-Q_d[i]) for i in range(_d)])
#prob.writeLP("show-Ldominance.lp")
status = prob.solve()
LpStatus[status]
result = value(prob.objective)
if result < 0:
return False
return True
def solve_jiang_guan_lp(return_samples, targets, asset_partition):
n = return_samples.shape[1]
prob = pulp.LpProblem('Jiang Guan DCCP Sample Approximation', pulp.LpMinimize)
x = [pulp.LpVariable('Asset {0:d} Weight'.format(i), 0.0, 1.0) for i in range(n)]
mu = calc_first_moment(return_samples)
prob += pulp.lpDot(mu, x), 'Sample Mean Return'
i = 0
for sample in return_samples:
prob += (pulp.lpDot(sample, x) >= targets[0],
'Global return target for sample {0:d}'.format(i))
i += 1
i = 0
j = 1
for assets in asset_partition:
for sample in return_samples:
prob += (pulp.lpSum([sample[k] * x[k] for k in range(n)]) >= targets[j],
'Return target for segment {0:d} for sample {1:d}'.format(j, i))
i += 1
j += 1
prob += (pulp.lpSum(x) == 1.0, 'Fully invested portfolio requirement')
prob.writeLP('JiangGuanDccp.lp')
prob.solve()
status = pulp.LpStatus[prob.status]
print 'Status: {0:s}'.format(status)
return np.array([v.varValue for v in prob.variables()]), status
def solve(g):
el = g.get_edge_list()
nl = g.get_node_list()
p = LpProblem('min_cost', LpMinimize)
capacity = {}
cost = {}
demand = {}
x = {}
for e in el:
capacity[e] = g.get_edge_attr(e[0], e[1], 'capacity')
cost[e] = g.get_edge_attr(e[0], e[1], 'cost')
for i in nl:
demand[i] = g.get_node_attr(i, 'demand')
for e in el:
x[e] = LpVariable("x"+str(e), 0, capacity[e])
# add obj
objective = lpSum (cost[e]*x[e] for e in el)
p += objective
# add constraints
for i in nl:
out_neig = g.get_out_neighbors(i)
in_neig = g.get_in_neighbors(i)
p += lpSum(x[(i,j)] for j in out_neig) -\
lpSum(x[(j,i)] for j in in_neig)==demand[i]
p.solve()
return x, value(objective)
def _GetTotalEnergyProblem(self, min_driving_force=0, objective=pulp.LpMinimize):
# Define and apply the constraints on the concentrations
ln_conc_lb, ln_conc_ub = self._MakeLnConcentratonBounds()
# Create the driving force variable and add the relevant constraints
A, b, _c = self._MakeDrivingForceConstraints(ln_conc_lb, ln_conc_ub)
lp = pulp.LpProblem("OBD", objective)
# ln-concentration variables
l = pulp.LpVariable.dicts("l", ["%d" % i for i in xrange(self.Nc)])
x = [l["%d" % i] for i in xrange(self.Nc)] + [min_driving_force]
total_g = pulp.LpVariable("g_tot")
for j in xrange(A.shape[0]):
row = [A[j, i] * x[i] for i in xrange(A.shape[1])]
lp += (pulp.lpSum(row) <= b[j, 0]), "energy_%02d" % j
total_g0 = float(self.dG0_r_prime * self.fluxes.T)
total_reaction = self.S * self.fluxes.T
row = [total_reaction[i, 0] * x[i] for i in xrange(self.Nc)]
lp += (total_g == total_g0 + pulp.lpSum(row)), "Total G"
lp.setObjective(total_g)
#lp.writeLP("../res/total_g.lp")
return lp, total_g
def setup():
# ... declare variables
x = ilp.LpVariable.dicts('x', nodes_vars.keys(), 0, 1, ilp.LpBinary)
y = ilp.LpVariable.dicts('y',
[(i, j) for i, j in edges] + [(j, i) for i, j in edges],
0, 1, ilp.LpBinary)
limits = defaultdict(int)
for i, j in edges:
limits[i] += 1
limits[j] += 1
# ... define the problem
prob = ilp.LpProblem("Factorizer", ilp.LpMinimize)
# ... define the constraints
for i in nodes_vars:
prob += ilp.lpSum(y[(i, j)] for j in nodes_vars if (i, j) in y) <= limits[i]*x[i]
for i, j in edges:
prob += y[(i, j)] + y[(j, i)] == 1
# ... define the objective function (min number of factorizations)
prob += ilp.lpSum(x[i] for i in nodes_vars)
return x, prob
def opt(C, X):
orderNum = len(X)
routeNum = len(C)
routeIdx = range(routeNum)
orderIdx = range(orderNum)
# print routeIdx,orderIdx
eps = 1.0 / 10 ** 7
print eps
var_choice = lp.LpVariable.dicts('route', routeIdx, cat='Binary')
# var_choice=lp.LpVariable.dicts('route',routeIdx,lowBound=0)#尝试松弛掉01变量
exceed_labor = lp.LpVariable('Number of routes exceed 1000', 0)
prob = lp.LpProblem("lastMile", lp.LpMinimize)
prob += exceed_labor * 100000 + lp.lpSum(var_choice[i] * C[i] for i in routeIdx)
prob += lp.lpSum(var_choice[i] for i in routeIdx) <= 1000 + exceed_labor + eps
for i in orderIdx:
prob += lp.lpSum(var_choice[j] for j in X[i]) >= (1 - eps)
prob.solve(lp.CPLEX(msg=0))
print "\n\nstatus:", lp.LpStatus[prob.status]
if lp.LpStatus[prob.status] != 'Infeasible':
obj = lp.value(prob.objective)
print "\n\nobjective:", obj
sol_list = [var_choice[i].varValue for i in routeIdx]
print "\n\nroutes:", (sum(sol_list))
# print "\n\noriginal problem:\n",prob
return obj, sol_list, lp.LpStatus[prob.status]
else:
return None, None, lp.LpStatus[prob.status]
def _create_lp(self):
''' See base class.
'''
self.model._create_lp()
self.lp_model_max_slack = self.model.lp_model.deepcopy()
input_slack_vars = pulp.LpVariable.dicts(
'input_slack', self.strongly_disposal_input_categories,
0, None, pulp.LpContinuous)
output_slack_vars = pulp.LpVariable.dicts(
'output_slack', self.strongly_disposal_output_categories,
0, None, pulp.LpContinuous)
# change objective function
self.lp_model_max_slack.sense = pulp.LpMaximize
self.lp_model_max_slack.objective = (
pulp.lpSum(list(input_slack_vars.values())) +
pulp.lpSum(list(output_slack_vars.values())))
# change constraints
for input_category in self.strongly_disposal_input_categories:
name = self.model._constraints[input_category]
self.lp_model_max_slack.constraints[name].addterm(
input_slack_vars[input_category], 1)
self.lp_model_max_slack.constraints[name].sense = pulp.LpConstraintEQ
for output_category in self.strongly_disposal_output_categories:
name = self.model._constraints[output_category]
self.lp_model_max_slack.constraints[name].addterm(
output_slack_vars[output_category], -1)
self.lp_model_max_slack.constraints[name].sense = pulp.LpConstraintEQ
def _MakeMDFProblemDual(self):
"""Create a CVXOPT problem for finding the Maximal Thermodynamic
Driving Force (MDF).
Does not set the objective function... leaves that to the caller.
Returns:
the linear problem object, and the four types of variables as arrays
"""
A, b, c, w, g, z, u = self._GetDualVariablesAndConstants()
x = w + g + z + u
lp = pulp.LpProblem("MDF_DUAL", pulp.LpMinimize)
cnstr_names = ["y_%02d" % j for j in xrange(self.Nr)] + \
["l_%02d" % j for j in xrange(self.Nc)] + \
["MDF"]
for i in xrange(A.shape[1]):
row = [A[j, i] * x[j] for j in xrange(A.shape[0])]
lp += (pulp.lpSum(row) == c[i, 0]), cnstr_names[i]
objective = pulp.lpSum([b[i] * x[i] for i in xrange(A.shape[0])])
lp.setObjective(objective)
lp.writeLP("res/mdf_dual.lp")
return lp, objective, w, g, z, u
def _MakeMDFProblem(self):
"""Create a PuLP problem for finding the Maximal Thermodynamic
Driving Force (MDF).
Does not set the objective function... leaves that to the caller.
Args:
c_range: a tuple (min, max) for concentrations (in M).
bounds: a list of (lower bound, upper bound) tuples for compound
concentrations.
Returns:
A tuple (dgf_var, motive_force_var, problem_object).
"""
# Create the driving force variable and add the relevant constraints
A, b, c = self._MakeDrivingForceConstraints()
lp = pulp.LpProblem("MDF", pulp.LpMaximize)
# ln-concentration variables
_l = pulp.LpVariable.dicts("lnC", ["%d" % i for i in xrange(self.Nc)])
B = pulp.LpVariable("B")
lnC = [_l["%d" % i] for i in xrange(self.Nc)] + [B]
for j in xrange(A.shape[0]):
row = [A[j, i] * lnC[i] for i in xrange(A.shape[1])]
lp += (pulp.lpSum(row) <= b[j, 0]), "energy_%02d" % j
objective = pulp.lpSum([c[i] * lnC[i] for i in xrange(A.shape[1])])
lp.setObjective(objective)
#lp.writeLP("res/MDF_primal.lp")
return lp, lnC
def fit(self, x, y):
classifiers = []
classifier_weights = []
clf = self.base_estimator
# get predictions of one classifer
clf.fit(x, y)
y_predict = clf.predict(x)
u = (y_predict == y).astype(int)
u[u == 0] = -1
for index, value in enumerate(u):
if value == -1:
print index
# solving linear programming
d = pp.LpVariable.dicts("d", range(len(y)), 0, 1)
prob = pp.LpProblem("LPboost", pp.LpMinimize)
prob += pp.lpSum(d) == 1 # constraint for sum of weights to be 1
# objective function
objective_vector = []
for index in range(len(y)):
objective_vector.append(d[index] * u[index])
prob += pp.lpSum(objective_vector)
print pp.LpStatus[prob.solve()]
for v in prob.variables():
if v.varValue > 0:
print v.name + "=" + str(v.varValue)
def K_dominnace_check_2(self, u_d, v_d, _inequalities):
"""
:param u_d: a d-dimensional vector(list) like [ 8.53149891 3.36436796]
:param v_d: tha same list like u_d
:param _inequalities: list of constraints on d-dimensional Lambda Polytope like
[[0, 1, 0], [1, -1, 0], [0, 0, 1], [1, 0, -1], [0.0, 1.4770889, -3.1250839]]
:return: True if u is Kdominance to v regarding given _inequalities otherwise False
"""
_d = len(u_d)
prob = LpProblem("Kdominance", LpMinimize)
lambda_variables = LpVariable.dicts("l", range(_d), 0)
for inequ in _inequalities:
prob += lpSum([inequ[j + 1] * lambda_variables[j] for j in range(0, _d)]) + inequ[0] >= 0
prob += lpSum([lambda_variables[i] * (u_d[i]-v_d[i]) for i in range(_d)])
#prob.writeLP("show-Ldominance.lp")
status = prob.solve()
LpStatus[status]
result = value(prob.objective)
if result < 0:
return False
return True
def good_annotation_locations(item, annotate_first=True):
"""Find the minimum number of annotations necessary to extract all the fields
Since annotations can be reviewed and modified later by the user we want to keep
just the minimum number of them.
Parameters
----------
item : Item
annotate_first : book
If true always annotate the first instance of the item in the page
Returns
-------
List[ItemLocation]
"""
# x[i] = 1 iff i-th item is representative
# A[i, j] = 1 iff i-th item contains the j-th field
#
# Solve:
# min np.sum(x)
# Subject to:
# np.all(np.dot(A.T, x) >= np.repeat(1, len(fields)))
index_locations = {location: i for i, location in enumerate(item.locations)}
P = pulp.LpProblem("good_annotation_locations", pulp.LpMinimize)
X = [pulp.LpVariable("x{0}".format(i), cat="Binary") for i in range(len(index_locations))]
if annotate_first:
P += X[0] == 1
P += pulp.lpSum(X)
for field in item.fields:
P += pulp.lpSum([X[index_locations[location.item]] for location in field.locations]) >= 1
P.solve()
return [i for (i, x) in enumerate(X) if x.value() == 1]
def _MakeMDFProblem(self):
"""Create a CVXOPT problem for finding the Maximal Thermodynamic
Driving Force (MDF).
Does not set the objective function... leaves that to the caller.
Returns:
the linear problem object, and the three types of variables as arrays
"""
A, b, c, y, l = self._GetPrimalVariablesAndConstants()
B = pulp.LpVariable("mdf")
x = y + l + [B]
lp = pulp.LpProblem("MDF_PRIMAL", pulp.LpMaximize)
cnstr_names = ["driving_force_%02d" % j for j in xrange(self.Nr_active)] + \
["covariance_var_ub_%02d" % j for j in xrange(self.Nr)] + \
["covariance_var_lb_%02d" % j for j in xrange(self.Nr)] + \
["log_conc_ub_%02d" % j for j in xrange(self.Nc)] + \
["log_conc_lb_%02d" % j for j in xrange(self.Nc)]
for j in xrange(A.shape[0]):
row = [A[j, i] * x[i] for i in xrange(A.shape[1])]
lp += (pulp.lpSum(row) <= b[j, 0]), cnstr_names[j]
objective = pulp.lpSum([c[i] * x[i] for i in xrange(A.shape[1])])
lp.setObjective(objective)
lp.writeLP("res/mdf_primal.lp")
return lp, objective, y, l, B
def min_one_norm(B,initial_seed,seed):
weight_initial = 1 / float(len(initial_seed))
weight_later_added = weight_initial / float(0.5)
difference = len(seed) - len(initial_seed)
[r,c] = B.shape
prob = pulp.LpProblem("Minimum one norm", pulp.LpMinimize)
indices_y = range(0,r)
y = pulp.LpVariable.dicts("y_s", indices_y, 0)
indices_x = range(0,c)
x = pulp.LpVariable.dicts("x_s", indices_x)
f = dict(zip(indices_y, [1.0]*r))
prob += pulp.lpSum(f[i] * y[i] for i in indices_y) # objective function
prob += pulp.lpSum(y[s] for s in initial_seed) >= 1
prob += pulp.lpSum(y[r] for r in seed) >= 1 + weight_later_added * difference
for j in range(r):
temp = dict(zip(indices_x, list(B[j,:])))
prob += pulp.lpSum(y[j] + (temp[k] * x[k] for k in indices_x)) == 0
prob.solve()
print "Status:", pulp.LpStatus[prob.status]
result = []
for var in indices_y:
result.append(y[var].value())
return result
def def_problem(self, queue_a):
"""define lp problem"""
n = self.n
tn = self.tn
tl = self.tl
b = self.b
iIdx = range(n)
tIdx = range(tn)
ti = range(n)
si = range(n)
Ti = range(n)
Si = range(n)
for i in xrange(n):
ti[i] = queue_a[i].at
si[i] = queue_a[i].tsize
Ti[i] = queue_a[i].ft
Si[i] = queue_a[i].size
iIdx[i] = str(i)
for t in xrange(tn):
tIdx[t] = str(t)
"""-----------------------------------
PuLP variable definition
varb--bi(t)
prob--objective and constraints
objective:
max:[0~n-1][0~ti-1])sigma bi(t)
constraints:
1.any t,[0~n-1] sigma bi(t) <= B
2.any i,[0-Ti] sigma bi(t)*tl>= si
3.any i,[0~T-1] sigma bi(t)*tl<= Si
------------------------------------"""
print "\ndefine lp variables"
self.varb = pulp.LpVariable.dicts('b',(iIdx,tIdx),0,b,cat='Integer')
print "define lp problem"
self.prob = pulp.LpProblem('Prefetching Schedule',pulp.LpMaximize)
print "define lp objective"
self.prob += pulp.lpSum([tl*self.varb[i][t] for i in iIdx for t in tIdx if int(t)<ti[int(i)]])
print "define constraints on B"
for t in tIdx:
self.prob += pulp.lpSum([self.varb[i][t] for i in iIdx]) <= b
print "define constraints on si"
for i in iIdx:
self.prob += pulp.lpSum([tl*self.varb[i][t] for t in tIdx if int(t)<=Ti[int(i)]]) >= si[int(i)]
print "define constraints on Si"
for i in iIdx:
self.prob += pulp.lpSum([tl*self.varb[i][t] for t in tIdx]) <= Si[int(i)]
def get_linear_program_solution(self, c, b, A, x):
prob = LpProblem("myProblem", LpMinimize)
prob += lpSum([xp*cp for xp, cp in zip(x, c)]), "Total Cost of Ingredients per can"
for row, cell in zip(A, b):
prob += lpSum(ap*xp for ap, xp in zip(row, x)) <= cell
solved = prob.solve()
return prob
def addFirstLastTaskConstraints(prob, numTasks, numDays, xihtVariables, xhitVariables):
for t in range(numDays):
xihtList = []
xhitList = []
for i in range(numTasks):
xihtList.append(xihtVariables[i][t])
xhitList.append(xhitVariables[i][t])
prob += pulp.lpSum(xihtList) == pulp.lpSum(xhitList)
prob += pulp.lpSum(xihtList) <= 1
prob += pulp.lpSum(xhitList) <= 1
def portfolio_lp(
weights, latest_prices, min_allocation=0.01, total_portfolio_value=10000
):
"""
For a long only portfolio, convert the continuous weights to a discrete allocation
using Mixed Integer Linear Programming. This can be thought of as a clever way to round
the continuous weights to an integer number of shares
:param weights: continuous weights generated from the ``efficient_frontier`` module
:type weights: dict
:param latest_prices: the most recent price for each asset
:type latest_prices: pd.Series or dict
:param min_allocation: any weights less than this number are considered negligible,
defaults to 0.01
:type min_allocation: float, optional
:param total_portfolio_value: the desired total value of the portfolio, defaults to 10000
:type total_portfolio_value: int/float, optional
:raises TypeError: if ``weights`` is not a dict
:raises TypeError: if ``latest_prices`` isn't a series
:raises ValueError: if not ``0 < min_allocation < 0.3``
:return: the number of shares of each ticker that should be purchased, along with the amount
of funds leftover.
:rtype: (dict, float)
"""
import pulp
if not isinstance(weights, dict):
raise TypeError("weights should be a dictionary of {ticker: weight}")
if not isinstance(latest_prices, (pd.Series, dict)):
raise TypeError("latest_prices should be a pd.Series")
if min_allocation > 0.3:
raise ValueError("min_allocation should be a small float")
if total_portfolio_value <= 0:
raise ValueError("total_portfolio_value must be greater than zero")
m = pulp.LpProblem("PfAlloc", pulp.LpMinimize)
vals = {}
realvals = {}
etas = {}
abss = {}
remaining = pulp.LpVariable("remaining", 0)
for k, w in weights.items():
if w < min_allocation:
continue
vals[k] = pulp.LpVariable("x_%s" % k, 0, cat="Integer")
realvals[k] = latest_prices[k] * vals[k]
etas[k] = w * total_portfolio_value - realvals[k]
abss[k] = pulp.LpVariable("u_%s" % k, 0)
m += etas[k] <= abss[k]
m += -etas[k] <= abss[k]
m += remaining == total_portfolio_value - pulp.lpSum(realvals.values())
m += pulp.lpSum(abss.values()) + remaining
m.solve()
results = {k: val.varValue for k, val in vals.items()}
return results, remaining.varValue
def execute_algorithm(self, input_data_set):
hosts = input_data_set.Hosts
vms = input_data_set.VirtualMachines
alert = input_data_set.Alert
prob = pulp.LpProblem("test",pulp.LpMinimize)
#if 1 host is enabled
u = []
x = []
for host in hosts:
ulp = pulp.LpVariable("used: %s" % host.Hostname,0,1,'Integer')
u.append(ulp)
innerX = []
x.append(innerX)
n = []
m = []
c = []
for vm in vms:
values = vm.getMetrics(host) #todo optimization
lpVar = pulp.LpVariable("host %s contains %s" % (vm.InstanceName, host.Hostname),0,1,'Integer')
innerX.append(lpVar)
prob += ulp >= lpVar
c.append([lpVar,values["C"]])
n.append([lpVar,values["N"]])
m.append([lpVar,values["M"]])
prob += (pulp.LpAffineExpression(c) <= 1)
prob += (pulp.LpAffineExpression(n) <= 1)
prob += (pulp.LpAffineExpression(m) <= 1)
#every vm on only one host
for i in range(len(vms)):
lps = [item[i] for item in x]
prob += (pulp.lpSum(lps) == 1)
prob += pulp.lpSum(u)
GLPK().solve(prob)
for v in prob.variables():
print v.name, "=", v.varValue
def add_stoichiometric_constraints(self, weights, S, compounds, reactions,
net_reaction):
"""
S is a NCxNR matrix where the rows represent compounds and the columns represent reactions.
b is the linear constraint vector, and is NCx1
compounds is the list of compounds from KEGG (NC long)
reactions is a list of pairs of RID and direction (NR long)
"""
self.weights = weights
self.S = S
self.compounds = compounds
self.reactions = reactions
assert S.shape[0] == len(self.compounds)
assert S.shape[1] == len(self.reactions)
self.cids = [c.cid for c in self.compounds]
self.physiological_conc = np.matrix(np.ones((1, len(self.compounds)))) * default_c_mid
if 1 in self.cids:
self.physiological_conc[0, self.cids.index(1)] = 1 # [H2O] must be set to 1 in any case
# reaction fluxes are the continuous variables
self.flux_vars = pulp.LpVariable.dicts("Flux",
[r.name for r in self.reactions],
lowBound=0,
upBound=self.flux_upper_bound,
cat=pulp.LpContinuous)
# add a linear constraint on the fluxes for each compound (mass balance)
for c, compound in enumerate(self.compounds):
reactions_with_c = list(np.nonzero(self.S[c, :])[1].flat)
if len(reactions_with_c) == 0:
continue
# If the compound participates in the net reaction, force the mass
# balance to be -1 times the desired net reaction coefficient
if compound.cid in net_reaction.sparse:
mass_balance = net_reaction.sparse[compound.cid]
else:
mass_balance = 0.0
# Sum of all fluxes involving this compounds times the stoichiometry
# of their reactions
cid_sum = pulp.lpSum([self.S[c, r] * self.flux_vars[self.reactions[r].name]
for r in reactions_with_c])
self.prob.addConstraint(cid_sum == mass_balance,
"C%05d_mass_balance" % compound.cid)
obj = pulp.lpSum([self.flux_vars[self.reactions[r].name]*weight
for (r, weight) in self.weights])
self.prob.setObjective(obj)
def opt_with_solver(node_ind, order_dict, travel_t, stay_t, pick_t, require_t, num,
mini_start, initial=None, load_check=True):
# order_dict = {ord:(ori_id, dest_id)}, ord in order_set, ori_id, dest_id in node_ind
# node_ind = range(n), travel_t = {(i,j): travel time between i and j}
# initial = (given start id, given load)
big_m = 10000
eps = 1.0/10**7
n = len(node_ind)
inter_n = [(i, j) for i in node_ind for j in node_ind if j != i]
off_time = lp.LpVariable('The route-off time')
p = lp.LpVariable.dicts('Punish cost', node_ind, lowBound=0)
x = lp.LpVariable.dicts('Route variable', inter_n, cat='Binary')
o = lp.LpVariable.dicts('Start-point flag', node_ind, cat='Binary')
d = lp.LpVariable.dicts('End-point flag', node_ind, cat='Binary')
a = lp.LpVariable.dicts('Arrival time', node_ind)
l = lp.LpVariable.dicts('Leave time', node_ind)
t = lp.LpVariable.dicts('Order count', node_ind, 0, n-1+eps)
if load_check:
load = lp.LpVariable.dicts('Arrival load', node_ind, 0, MAX_LOADS+eps)
else:
load = lp.LpVariable.dicts('Arrival load', node_ind, 0)
prob = lp.LpProblem('Optimize a route', lp.LpMinimize)
# Objective
prob += off_time + lp.lpSum(p[i] for i in node_ind)
# Constraints
for j in node_ind:
prob += lp.lpSum(x[(i, j)] for i in node_ind if i != j) == 1 - o[j]
for i in node_ind:
prob += lp.lpSum(x[(i, j)] for j in node_ind if j != i) == 1 - d[i]
prob += lp.lpSum(o[i] for i in node_ind) == 1
prob += lp.lpSum(d[i] for i in node_ind) == 1
if not (initial is None):
prob += o[initial[0]] == 1
prob += load[initial[0]] == initial[1]
for order in order_dict:
od = order_dict[order]
prob += a[od[1]] >= l[od[0]]
for i in node_ind:
prob += off_time >= l[i]
prob += l[i] >= a[i] + stay_t[i]
prob += l[i] >= pick_t[i]
prob += a[i] >= mini_start[i]
prob += p[i] >= PUNISH_CO*(a[i] - require_t[i])
for i, j in inter_n:
prob += a[j] >= l[i] + travel_t[(i, j)] + big_m*(x[(i, j)] - 1)
prob += t[j] >= t[i] + 1 + big_m*(x[(i, j)] - 1)
prob += load[j] >= load[i] + num[i] + big_m*(x[(i, j)] - 1)
prob.solve(lp.CPLEX(msg=1, timelimit=CPLEX_TIME_LIMIT, options=['set logfile cplex/cplex%d.log' % os.getpid()]))
# set threads 100
if lp.LpStatus[prob.status] != 'Infeasible':
sol_list = [int(round(t[i].varValue)) for i in node_ind]
return lp.value(prob.objective), sol_list, lp.LpStatus[prob.status]
else:
return None, None, lp.LpStatus[prob.status]
def create_lscp_model(coverage_dict, model_file=None, delineator="$", ):
"""
Creates a LSCP (Location set covering problem) using the provided coverage and
parameters. Writes a .lp file which can be solved with Gurobi
Church, R., & Murray, A. (2009). Coverage Business Site Selection, Location
Analysis, and GIS (pp. 209-233). Hoboken, New Jersey: Wiley.
:param coverage_dict: (dictionary) The coverage to use to generate the model
:param model_file: (string) The model file to output
:param delineator: (string) The character(s) to use to delineate the layer from the ids
:return: (Pulp problem) The generated problem to solve
"""
validate_coverage(coverage_dict, ["coverage"], ["binary"])
if not isinstance(coverage_dict, dict):
raise TypeError("coverage_dict is not a dictionary")
if model_file and not (isinstance(model_file, str)):
raise TypeError("model_file is not a string")
if not isinstance(delineator, str):
raise TypeError("delineator is not a string")
# create the variables
demand_vars = {}
for demand_id in coverage_dict["demand"]:
demand_vars[demand_id] = pulp.LpVariable("Y{}{}".format(delineator, demand_id), 0, 1, pulp.LpInteger)
facility_vars = {}
for facility_type in coverage_dict["facilities"]:
facility_vars[facility_type] = {}
for facility_id in coverage_dict["facilities"][facility_type]:
facility_vars[facility_type][facility_id] = pulp.LpVariable(
"{}{}{}".format(facility_type, delineator, facility_id), 0, 1, pulp.LpInteger)
# create the problem
prob = pulp.LpProblem("LSCP", pulp.LpMinimize)
# Create objective, minimize number of facilities
to_sum = []
for facility_type in coverage_dict["facilities"]:
for facility_id in coverage_dict["facilities"][facility_type]:
to_sum.append(facility_vars[facility_type][facility_id])
prob += pulp.lpSum(to_sum)
# add coverage constraints
for demand_id in coverage_dict["demand"]:
to_sum = []
for facility_type in coverage_dict["demand"][demand_id]["coverage"]:
for facility_id in coverage_dict["demand"][demand_id]["coverage"][facility_type]:
to_sum.append(facility_vars[facility_type][facility_id])
# Hack to get model to "solve" when infeasible with GLPK.
# Pulp will automatically add dummy variables when the sum is empty, since these are all the same name,
# it seems that GLPK doesn't read the lp problem properly and fails
if not to_sum:
to_sum = [pulp.LpVariable("__dummy{}{}".format(delineator, demand_id), 0, 0, pulp.LpInteger)]
prob += pulp.lpSum(to_sum) >= 1, "D{}".format(demand_id)
if model_file:
prob.writeLP(model_file)
return prob
def pe185():
"""
Modelling as an integer programming problem.
Then using PuLP to solve it. It's really fast, just 0.24 seconds.
For details, see https://pythonhosted.org/PuLP/index.html
"""
from pulp import LpProblem, LpVariable, LpMinimize, LpInteger, lpSum, value
constraints = [
('2321386104303845', 0),
('3847439647293047', 1),
('3174248439465858', 1),
('8157356344118483', 1),
('6375711915077050', 1),
('6913859173121360', 1),
('4895722652190306', 1),
('5616185650518293', 2),
('4513559094146117', 2),
('2615250744386899', 2),
('6442889055042768', 2),
('2326509471271448', 2),
('5251583379644322', 2),
('2659862637316867', 2),
('5855462940810587', 3),
('9742855507068353', 3),
('4296849643607543', 3),
('7890971548908067', 3),
('8690095851526254', 3),
('1748270476758276', 3),
('3041631117224635', 3),
('1841236454324589', 3)
]
VALs = map(str, range(10))
LOCs = map(str, range(16))
choices = LpVariable.dicts("Choice", (LOCs, VALs), 0, 1, LpInteger)
prob = LpProblem("pe185", LpMinimize)
prob += 0, "Arbitrary Objective Function"
for s in LOCs:
prob += lpSum([choices[s][v] for v in VALs]) == 1, ""
for c, n in constraints:
prob += lpSum([choices[str(i)][v] for i,v in enumerate(c)]) == n, ""
prob.writeLP("pe185.lp")
prob.solve()
res = int(''.join(v for s in LOCs for v in VALs if value(choices[s][v])))
# answer: 4640261571849533
return res
def formulate(cp):
prob = dippy.DipProblem("Coke",
display_mode = 'xdot',
# layout = 'bak',
display_interval = None,
)
# create variables
LOC_SIZES = [(l, s) for l in cp.LOCATIONS
for s in cp.SIZES]
buildVars = LpVariable.dicts("Build", LOC_SIZES, cat=LpBinary)
# create arcs
flowVars = LpVariable.dicts("Arcs", cp.ARCS)
BIG_M = max(sum(cp.supply.values()), sum(cp.demand.values()))
for a in cp.ARCS:
flowVars[a].bounds(0, BIG_M)
# objective
prob += 1e6 * lpSum(buildVars[(l, s)] * cp.build_costs[s] \
for (l, s) in LOC_SIZES) + \
lpSum(flowVars[(s, d)] * cp.transport_costs[(s, d)] \
for (s, d) in cp.ARCS), "min"
# plant availability - assumes that SIZES are numeric,
# which they should be
for loc in cp.LOCATIONS:
prob += lpSum(flowVars[(loc, i)] for i in cp.CUSTOMERS) \
<= lpSum(buildVars[(loc, s)] * s for s in cp.SIZES)
# one size
for loc in cp.LOCATIONS:
prob += lpSum(buildVars[(loc, s)] for s in cp.SIZES) == 1
# conserve flow (mines)
# flows are in terms of tonnes of coke
for m in cp.MINES:
prob += lpSum(flowVars[(m, j)] for j in cp.LOCATIONS) \
<= cp.supply[m]
# conserve flow (locations)
# convert from coal to coke
for loc in cp.LOCATIONS:
prob += lpSum(flowVars[(m, loc)] for m in cp.MINES) - \
cp.conversion_factor * \
lpSum(flowVars[(loc, c)] for c in cp.CUSTOMERS) \
>= 0
for c in cp.CUSTOMERS:
prob += lpSum(flowVars[(loc, c)] for loc in cp.LOCATIONS) \
>= cp.demand[c]
prob.cp = cp
prob.buildVars = buildVars
prob.flowVars = flowVars
return prob
def knapsack01(obj, weights, capacity):
""" 0/1 knapsack solver, maximizes profit. weights and capacity integer """
debug_subproblem = False
assert len(obj) == len(weights)
n = len(obj)
if n == 0:
return 0, []
if debug_subproblem:
relaxation = LpProblem('relaxation', LpMaximize)
relax_vars = [str(i) for i in range(n)]
var_dict = LpVariable.dicts("", relax_vars, 0, 1, LpBinary)
relaxation += (lpSum(var_dict[str(i)] * weights[i] for i in range(n))
<= capacity)
relaxation += lpSum(var_dict[str(i)] * obj[i] for i in range(n))
relaxation.solve()
relax_obj = value(relaxation.objective)
solution = [i for i in range(n) if var_dict[str(i)].varValue > tol ]
print relax_obj, solution
c = [[0]*(capacity+1) for i in range(n)]
added = [[False]*(capacity+1) for i in range(n)]
# c [items, remaining capacity]
# important: this code assumes strictly positive objective values
for i in range(n):
for j in range(capacity+1):
if (weights[i] > j):
c[i][j] = c[i-1][j]
else:
c_add = obj[i] + c[i-1][j-weights[i]]
if c_add > c[i-1][j]:
c[i][j] = c_add
added[i][j] = True
else:
c[i][j] = c[i-1][j]
# backtrack to find solution
i = n-1
j = capacity
solution = []
while i >= 0 and j >= 0:
if added[i][j]:
solution.append(i)
j -= weights[i]
i -= 1
return c[n-1][capacity], solution
def addConnectivityConstraints(prob, xijtVariables, xihtVariables, xhitVariables, yitkVariables, numTasks, numDays, yiVariables):
for t in range(numDays):
for i in range(numTasks):
xjitList = []
xijtList = []
for j in range(numTasks):
if i != j:
xjitList += xijtVariables[j][i][t]
xijtList += xijtVariables[i][j][t]
prob += pulp.lpSum(xijtList) + xihtVariables[i][t] == pulp.lpSum(xjitList) + xhitVariables[i][t] # for job i sum xijt = sum xjit
yitkList = []
for k in range(len(yitkVariables[i][t])):
yitkList += yitkVariables[i][t][k]
prob += pulp.lpSum(xijtList) + xihtVariables[i][t] == pulp.lpSum(yitkList) # for job i sum xijt = yitk
def prepare_problem(A, b, c):
"""
Создаёт проблемы по заданным матрице A,
вектору b и оптимизируемой функции
"""
problem = LpProblem(sense = pulp.constants.LpMaximize)
x_list = [LpVariable("x" + str(i + 1), 0) for i in xrange(len(A[0]))]
problem += lpSum(ci * xi for ci, xi in zip(c, x_list))
for aj, bj in zip(A, b):
problem += lpSum([aij * xi for aij, xi in zip(aj, x_list)]) == bj
return problem
def _MakeOBDProblemDual(self):
"""Create a CVXOPT problem for finding the Maximal Thermodynamic
Driving Force (OBD).
Does not set the objective function... leaves that to the caller.
Args:
c_range: a tuple (min, max) for concentrations (in M).
bounds: a list of (lower bound, upper bound) tuples for compound
concentrations.
Returns:
A tuple (dgf_var, motive_force_var, problem_object).
"""
# Define and apply the constraints on the concentrations
ln_conc_lb, ln_conc_ub = self._MakeLnConcentratonBounds()
# Create the driving force variable and add the relevant constraints
A, b, c = self._MakeDrivingForceConstraints(ln_conc_lb, ln_conc_ub)
lp = pulp.LpProblem("OBD", pulp.LpMinimize)
w = pulp.LpVariable.dicts("w",
["%d" % i for i in xrange(self.Nr)],
lowBound=0)
z = pulp.LpVariable.dicts("z",
["%d" % i for i in xrange(self.Nc)],
lowBound=0)
u = pulp.LpVariable.dicts("u",
["%d" % i for i in xrange(self.Nc)],
lowBound=0)
y = [w["%d" % i] for i in xrange(self.Nr)] + \
[z["%d" % i] for i in xrange(self.Nc)] + \
[u["%d" % i] for i in xrange(self.Nc)]
for i in xrange(A.shape[1]):
row = [A[j, i] * y[j] for j in xrange(A.shape[0])]
lp += (pulp.lpSum(row) == c[i, 0]), "dual_%02d" % i
objective = pulp.lpSum([b[i] * y[i] for i in xrange(A.shape[0])])
lp.setObjective(objective)
#lp.writeLP("../res/obd_dual.lp")
return lp, w, z, u
def solve_under_coverage(graph, min_coverage=80):
prob = LpProblem("granularity selection", LpMinimize)
codelet_vars = LpVariable.dicts("codelet",
graph,
lowBound=0,
upBound=1,
cat=LpInteger)
# Objective function: minimize the total replay cost of selected codelets
# Compute replay time
for n,d in graph.nodes(data=True):
d['_total_replay_cycles'] = 0
for inv in d['_invocations']:
d['_total_replay_cycles'] = d['_total_replay_cycles'] + float(inv["Invivo (cycles)"])
prob += lpSum([codelet_vars[n]*d['_total_replay_cycles'] for n,d in graph.nodes(data=True)])
# and with good coverage
prob += (lpSum([codelet_vars[n]*d['_coverage'] for n,d in graph.nodes(data=True)]) >= min_coverage)
# selected codelets should match
for n,d in graph.nodes(data=True):
if not d['_matching']:
prob += codelet_vars[n] == 0
# Finally we should never include both the children and the parents
for dad in graph.nodes():
for son in graph.nodes():
if not dad in nx.ancestors(graph, son):
continue
# We cannot select dad and son at the same time
prob += codelet_vars[dad] + codelet_vars[son] <= 1
#prob.solve(GLPK())
prob.solve()
if (LpStatus[prob.status] != 'Optimal'):
raise Unsolvable()
for v in prob.variables():
assert v.varValue == 1.0 or v.varValue == 0.0
if v.varValue == 1.0:
for n,d in graph.nodes(data=True):
if ("codelet_"+str(n)) == v.name:
d["_selected"] = True
yield n
def formulate(bpp):
prob = dippy.DipProblem("Bin Packing",
display_mode = 'xdot',
# layout = 'bak',
display_interval = None,
)
assign_vars = LpVariable.dicts("x",
[(i, j) for i in bpp.BINS
for j in bpp.ITEMS],
cat=LpBinary)
use_vars = LpVariable.dicts("y", bpp.BINS, cat=LpBinary)
waste_vars = LpVariable.dicts("w", bpp.BINS, 0, None)
prob += lpSum(waste_vars[i] for i in bpp.BINS), "min_waste"
for j in bpp.ITEMS:
prob += lpSum(assign_vars[i, j] for i in bpp.BINS) == 1
for i in bpp.BINS:
prob.relaxation[i] += (lpSum(bpp.volume[j] * assign_vars[i, j]
for j in bpp.ITEMS) + waste_vars[i]
== bpp.capacity * use_vars[i])
for i in bpp.BINS:
for j in bpp.ITEMS:
prob.relaxation[i] += assign_vars[i, j] <= use_vars[i]
if Bin_antisymmetry:
for m in range(0, len(bpp.BINS) - 1):
prob += use_vars[bpp.BINS[m]] >= use_vars[bpp.BINS[m + 1]]
if Item_antisymmetry:
for m in range(0, len(bpp.BINS)):
for n in range(0, len(bpp.ITEMS)):
if m > n:
i = bpp.BINS[m]
j = bpp.ITEMS[n]
prob += assign_vars[i, j] == 0
# Attach the problem data and variable dictionaries
# to the DipProblem
prob.bpp = bpp
prob.assign_vars = assign_vars
prob.use_vars = use_vars
prob.waste_vars = waste_vars
return prob
def IP_solution(self):
"""
This method implements the IP solution.
Args:
- self: Sudoku instance, to be solved using IP.
Returns:
- flag: boolean, whether the Sudoku is solvable.
If True, the completed Sudoku is available through the attribute
"_solution" and the completion time through "_duration".
"""
# List of available digits
Digits = ["1", "2", "3", "4", "5", "6", "7", "8", "9"]
Values = Digits
Rows = Digits
Columns = Digits
# Creating the block list
Blocks = []
for i in range(3):
for j in range(3):
Blocks += [[(Rows[3 * i + k], Columns[3 * j + l])
for k in range(3) for l in range(3)]]
# We need to create the IP problem
prob = pulp.LpProblem("Sudoku solver", pulp.LpMinimize)
# Then we create the problem variables
choices = pulp.LpVariable.dicts("Choice", (Rows, Columns, Values), 0,
1, pulp.LpInteger)
# We define the objective function which is 0 here
prob += 0, "Objective Function"
# Defining the constraint: only one value can be put in a cell
for r in Rows:
for c in Columns:
prob += pulp.lpSum([choices[r][c][v] for v in Values]) == 1, ""
for v in Values:
# Each value must occur exactly once in each row
for r in Rows:
prob += pulp.lpSum([choices[r][c][v]
for c in Columns]) == 1, ""
# Each value must occur exactly once in each column
for c in Columns:
prob += pulp.lpSum([choices[r][c][v] for r in Rows]) == 1, ""
# Each value must occur exactly once in each block
for b in Blocks:
prob += pulp.lpSum([choices[r][c][v] for (r, c) in b]) == 1, ""
# We need to add the starting numbers as constraints
grid = self.grid
for r in range(len(grid)):
for c in range(len(grid[0])):
value = grid[r][c]
if value != 0:
prob += choices[str(int(r + 1))][str(int(c + 1))][str(
int(value))] == 1, ""
solve_value = prob.solve()
# flag is True is solve_value evaluates to 1 i.e. the Sudoku can be
# solved
flag = solve_value == 1
if flag:
solution_grid = np.zeros((9, 9))
for r in Rows:
for c in Columns:
for v in Values:
if choices[r][c][v].value() == 1.0:
solution_grid[int(r) - 1][int(c) - 1] = v
self._solution = solution_grid
return flag
def set_objective(self, variables, coefficients):
self.prob += lpSum([variable * coefficient for variable, coefficient in zip(variables, coefficients)])
def baruah_algorithm(wcet):
task_count, core_count = wcet.shape
# Create the model
model = LpProblem(name="LPR-feas", sense=LpMinimize)
# Initialize the decision variables
x_variables = dict()
for t in range(task_count):
for j in range(core_count):
if (t, j) not in x_variables:
x_variables[(t, j)] = LpVariable(name="x_task{}_cpu{}".format(
t, j),
lowBound=0)
u_variable = LpVariable(name="U", lowBound=0)
# Add the constraints to the model
for t in range(task_count):
model += (lpSum([x_variables[(t, j)] for j in range(core_count)]) == 1,
"One_assignment_constraint{}".format(t))
for j in range(core_count):
model += (lpSum(
[x_variables[(t, j)] * wcet[t, j]
for t in range(task_count)]) <= u_variable,
"EDF constraint on CPU{}".format(j))
# Add the objective function to the model
obj_func = lpSum(u_variable)
model += obj_func
# The problem data is written to an .lp file
# model.writeLP("Baruah_LP.lp")
# Solve the problem
# status = model.solve()
status = model.solve(PULP_CBC_CMD(msg=0, timeLimit=900))
#print("-------------------------------------------------------")
#print(f"status: {model.status}, {LpStatus[model.status]}")
#print(f"objective: {model.objective.value()}")
# create bipartite graph
B = nx.Graph()
partitioning = dict()
for t in range(task_count):
B.add_nodes_from(["task{}".format(t)], bipartite=0)
partitioning["task{}".format(t)] = None
for j in range(core_count):
B.add_nodes_from(["cpu{}".format(j)], bipartite=1)
for var in model.variables():
#print(f"{var.name}: {var.value()}")
if var.name == "U":
if var.value() > 1:
#raise Exception("Invalid LP solution!")
return np.inf, "Invalid LP solution!"
else:
if var.value() > 0:
#print(var.name)
string_parts = var.name.split("_")
task = int(string_parts[1][4:])
cpu = int(string_parts[2][3:])
B.add_edges_from([("task{}".format(task), "cpu{}".format(cpu))
])
#X, Y = bipartite.sets(B)
draw = False
try:
X, Y = bipartite.sets(B)
except:
draw = False
#plt.close()
#nx.draw(B, with_labels=True)
#plt.show()
X = {n for n, d in B.nodes(data=True) if d["bipartite"] == 0}
Y = set(B) - X
#nx.draw(B, with_labels=True, pos = nx.drawing.layout.bipartite_layout(B, X) )
#plt.show()
#plt.close()
#nx.draw(B, with_labels=True)
#plt.show()
# delete exact mappings' tasks
task_vertices_to_delete = []
for task in X:
#print("Degree of {} is {}".format(task,B.degree[task]))
if B.degree[task] == 1:
task_vertices_to_delete.append(task)
partitioning[task] = list(B.neighbors(task))[0]
for task in task_vertices_to_delete:
B.remove_node(task)
#plt.close()
#if draw:
# nx.draw(B, with_labels=True)
# plt.show()
# TODO: find cycles
# create bipartite graph
"""
B = nx.Graph()
B.add_nodes_from(["task0"], bipartite=0)
B.add_nodes_from(["cpu0"], bipartite=1)
B.add_nodes_from(["task1"], bipartite=0)
B.add_nodes_from(["cpu1"], bipartite=1)
B.add_edges_from([("task0", "cpu0")])
B.add_edges_from([("task0", "cpu1")])
B.add_edges_from([("task1", "cpu0")])
B.add_edges_from([("task1", "cpu1")])
"""
cycle = False
try:
cycle = list(nx.find_cycle(B, orientation="ignore"))
print(cycle)
cycle = True
except:
pass
if cycle:
raise Exception("Cycle is found")
#unassigned_tasks, cpus = bipartite.sets(B)
unassigned_tasks = {
n
for n, d in B.nodes(data=True) if d["bipartite"] == 0
}
cpus = set(B) - unassigned_tasks
while len(unassigned_tasks) != 0:
for t, c in partitioning.items():
if c != None:
try:
B.remove_node(t)
except nx.exception.NetworkXError:
pass
root = None
tasks = {n for n, d in B.nodes(data=True) if d["bipartite"] == 0}
cpus = set(B) - tasks
for task in tasks:
if B.degree[task] == 1:
root = task
if root == None:
root_cpu = random.choice(list(cpus))
B.add_node("arbitrary_task", bipartite=0)
B.add_edges_from([("arbitrary_task", root_cpu)])
root = "arbitrary_task"
unassigned_tasks.add("arbitrary_task")
#if draw:
# plt.close()
# nx.draw(B, with_labels=True)
# plt.show()
visited_nodes = [root]
unvisited_nodes = []
task_cpus = B.neighbors(root)
task_cpus = list(task_cpus)
unvisited_nodes.extend(task_cpus)
unvisited_nodes = list(set(unvisited_nodes))
mapped_cpu = random.choice(task_cpus)
#print("task {} -> cpu {}".format(root,mapped_cpu))
partitioning[root] = mapped_cpu
unassigned_tasks.remove(root)
while unvisited_nodes != []:
start_node = unvisited_nodes[0]
visited_nodes.append(start_node)
unvisited_nodes.remove(start_node)
neigh = B.neighbors(start_node)
for node in neigh:
if node not in visited_nodes:
unvisited_nodes.append(node)
if "task" in start_node:
task_cpus = B.neighbors(start_node)
task_cpus = [i for i in task_cpus if i not in visited_nodes]
mapped_cpu = random.choice(task_cpus)
#print("task {} -> cpu {}".format(start_node, mapped_cpu))
partitioning[start_node] = mapped_cpu
unassigned_tasks.remove(start_node)
#for name, constraint in model.constraints.items():
# print(f"{name}: {constraint.value()}")
#print("Solver: {}".format(model.solver))
if status == 1:
used_cpus = []
try:
partitioning.pop("arbitrary_task")
except KeyError:
pass
#check if partitioning valid
for task, cpu in partitioning.items():
if cpu == None:
raise Exception("There is a no mapping")
used_cpus.append(cpu)
for cpu_iter in range(core_count):
cpu = "cpu{}".format(cpu_iter)
sum_utilization = 0
for task, cpu_ in partitioning.items():
if cpu == cpu_:
sum_utilization += wcet[int(task[4:])][cpu_iter]
if sum_utilization > 1.0001:
return np.inf, "Overloaded cpu '{}': {}".format(
cpu, sum_utilization)
used_cpus = list(set(used_cpus))
return len(used_cpus), "OK"
else:
# print("OPT is not optimal solution! Reason: {}".format(LpStatus[model.status]))
return np.inf, LpStatus[model.status]
def accumulate(var, i) -> pulp.pulp.LpAffineExpression:
return pulp.lpSum([var[k] for k in range(i + 1)])
prob = dippy.DipProblem("Coke", LpMinimize)
# create variables
buildVars = LpVariable.dicts("Build", LOC_SIZES, None, \
None, LpBinary)
prob.buildVars = buildVars
# create arcs
flowVars = LpVariable.dicts("Arcs", ARCS)
for a in ARCS:
flowVars[a].bounds(0, BIG_M)
prob.SIZES = SIZES
# objective
prob += 1e6 * lpSum(buildVars[(l, s)] * SIZE_COSTS[s] \
for (l, s) in LOC_SIZES) + \
lpSum(flowVars[(s, d)] * ARC_COSTS[(s, d)] \
for (s, d) in ARCS), "min"
# plant availability
for loc in LOCATIONS:
prob += lpSum(flowVars[(loc, i)] for i in CUSTOMERS) \
<= lpSum(buildVars[(loc, s)] *s for s in SIZES)
# one size
for loc in LOCATIONS:
prob += lpSum(buildVars[(loc, s)] for s in SIZES) == 1
# conserve flow (mines)
# flows are in terms of tonnes of coke
for m in MINES:
def __addVarConstraint(self, zInit):
'''
Add constraints on the variables (Includes state transition constraint)
'''
# Constraints on state parameters
# x[0] == xInit
for x_var, xi in zip(self.x[0].values(), self.xInit):
self.prob.addConstraint(x_var == xi)
for x_var, xi in zip(self.x[self.__N].values(), self.xT):
self.prob.addConstraint(x_var == xi)
if False: #if self.margin != None:
# Add the boundary constraints
xmin = min(self.xInit[0], self.xT[0]) - self.margin
xmax = max(self.xInit[0], self.xT[0]) + self.margin
ymin = min(self.xInit[1], self.xT[1]) - self.margin
ymax = max(self.xInit[1], self.xT[1]) + self.margin
for idx in range(1, self.__N):
for x_var, xm in zip(self.x[idx].values(), [xmin, ymin]):
self.prob.addConstraint(x_var >= xm)
for x_var, xm in zip(self.x[idx].values(), [xmax, ymax]):
self.prob.addConstraint(x_var <= xm)
# Constraints on intermediate variables
if self.receding_horizon:
limit = self.__N - 1
else:
limit = self.__N
for k in range(limit):
# absu >= u
# absu + u >= 0
for i in range(self.__nu):
self.prob.addConstraint(self.absu[k][i] - self.u[k][i] >= 0)
self.prob.addConstraint(self.absu[k][i] + self.u[k][i] >= 0)
# State Transition modelled as a constraint
# x[k+1] == A*x[k] + B*u[:,k]
for x_var, a, b in zip(self.x[k + 1].values(), self.__A, self.__B):
self.prob.addConstraint((x_var - pulp.lpSum([(ai * xi) for ai, xi in zip(a, self.x[k].values())]) \
- pulp.lpSum([(bi * ui) for bi, ui in zip(b, self.u[k].values())])) == 0)
# Lower bound on the horizon radius
if self.receding_horizon:
self.prob.addConstraint(self.dlast >= 0)
# Constraints the distrance between adjacent points
for kk in range(1, self.__N + 1 - self.receding_horizon):
currx = self.x[kk].values()
prevx = self.x[kk - 1].values()
self.prob.addConstraint(self.d[kk - 1] >= 0)
for side in range(self.poly_nsides):
# parameters that determine the side of the polygon
line_angle = [math.cos(2*side*math.pi/self.poly_nsides), \
math.sin(2*side*math.pi/self.poly_nsides)]
# Add constraints between the last step and the goal point
# This ensures that the goal is within the horizon of the last step
self.prob.addConstraint(
pulp.lpSum([
m * (x1 - x2)
for m, x1, x2 in zip(line_angle, currx, prevx)
]) <= self.d[kk - 1])
if self.receding_horizon:
xlaststep = self.x[self.__N - 1].values()
xgoal = self.x[self.__N].values()
for side in range(500):
# parameters that determine the side of the polygon
line_angle = [math.cos(2*side*math.pi/self.poly_nsides), \
math.sin(2*side*math.pi/self.poly_nsides)]
# Add constraints between the last step and the goal point
# This ensures that the goal is within the horizon of the last step
self.prob.addConstraint(
pulp.lpSum([
m * (x1 - x2)
for m, x1, x2 in zip(line_angle, xgoal, xlaststep)
]) <= self.dlast)
# \sum_{i} z_{i} == dim(z_{i}) - 1 constraint
for k in range(limit):
for i in range(self.__nObst):
self.prob.addConstraint(pulp.lpSum([self.z[k][i][j] for j in range(self.__dObst)]) == \
self.__dObst-1, name='z_%d_%d'%(k, i))
def addAllZConstraints(self):
for k in range(self.__N):
for i in range(self.__nObst):
self.prob.addConstraint(pulp.lpSum([self.z[k][i][j] for j in range(self.__dObst)]) == \
self.__dObst-1, name='z_%d_%d'%(k, i))
return
#Csums=pulp.LpVariable.dicts("line_prod",(prod_line,orderList),0,None, pulp.LpInteger)
#r=pulp.LpVariable.dicts("release",(prod_line,orderList),0)
#compD=pulp.LpVariable.dicts("compDate",(prod_line,orderList),0)
#CLines=pulp.LpVariable("CLines",[j for j in line_date] ,0) #total amount of all lines by day
#Pday=pulp.LpVariable.dicts("Processdays",(prod_line,orderList),0)
#AM=pulp.LpVariable.dicts('planAmountEveryorder',(prod_line,orderList,plan_dates),0)
#Create the 'prob' variable to contain the problem data
#prob=pulp.LpProblem("The APS Problem",pulp.LpMinimize)
#objective function： consider order priority and leadtime
#eps=1e-2
#lambda compD[l][o] if compD[l][o]<=len(plan_dates) else len(plan_dates)
#adt.model_total_volume(order_spd[(order_spd['order_id']==o)&(order_spd['line_no']==l)][['day_process','num_by_day']],adt.prod_days(compD[l][o],len(plan_dates),r[l][o]),len(plan_dates))
#+pulp.lpSum([mp[i]*x[i][(i,j,t)]*(t-g[i][j]) for i in modelList for j in Md[i] for t in T[i][j] if t>g[i][j]])+
#pulp.lpSum([h[i][(i,t)]*t for i in modelList for t in Tm[i]])
prob+=pulp.lpSum([x[i][(i,j,t)]*(t-g[i][j]) for i in modelList for j in Md[i] for t in T[i][j] if t>g[i][j]])
#pulp.lpSum([orderPool['priority'][o]*orderPool['order_type'][o]*\
#Csums[l][o] for o in orderList for l in prod_line]) pulp.lpSum([x[i][(i,j,t)]*(t-g[i][j]) for i in modelList for j in Md[i] for t in T[i][j] if t>g[i][j]])
#prob+=Cmax+eps*lpSum([r[j] for j in order_line])-eps*[compD[j] for j in order_line]
#The constraints
#1. every order has to be completed before due date
#2. relationships between release date and due date
#3. release date>=max(0,epst-plan_dates[0])
#4.fixed sum across day and lines equal total num of orders
for i in modelList:
#prob+=pulp.lpSum([LS[i][i][l] for l in prod_line])>=1
#prob+=pulp.lpSum([k[i][(i,l,m)] for l in prod_line for m in P[i][l][0]])==pulp.lpSum([LS[i][(i,j,l)] for j in Md[i] for l in prod_line])
#限定分割总量
#prob+=pulp.lpSum([k[i][(i,l,m)]*P[i][l][1][m] for l in model_line[model_line['model_no']==i]['line_no'] for m in P[i][l][0]])==modelSum[i]
pulp.LpMaximize)
## Two variables: Cones & Sorbets
icecream_types = ['Cones', 'Sorbets']
## Declare variables
icecream_types = pulp.LpVariable.dicts("Icecream type",
((i) for i in icecream_types),
lowBound=0,
cat='Continuous')
## Maximize profit
model_icecream += pulp.lpSum(3 * icecream_types['Cones'] +
2 * icecream_types['Sorbets'])
## Constraints
MaxSupply = {'Cones': 10, 'Sorbets': 15}
## MaxSupply
for i in icecream_types:
model_icecream += icecream_types[i] <= MaxSupply[i]
print(icecream_types['Cones'])
print(model_icecream)
model += pulp.lpSum([ing_weight['premium', j]
def multiple_pairs(dist_matrices,
voi_ind_to_S_sets,
threshold=None,
api='py-mip',
solver='pulp',
variable_num_tol=0.001,
max_seconds=np.inf):
"""
Doc string to be written.
"""
voi_indices = list(voi_ind_to_S_sets.keys())
n_voi = len(voi_indices)
S_sets = list(voi_ind_to_S_sets.values())
M = np.max(np.max(abs(dist_matrices), axis=0))
a, b, c = dist_matrices.shape
if b == c:
J = a
n = b
dist_matrices = dist_matrices.transpose((1, 2, 0))[voi_indices]
else:
n = b
J = c
voi_to_Q_indices = {
voi: np.array([
int(i) for i in np.concatenate((range(0, voi), range(voi + 1, n)))
if i not in voi_ind_to_S_sets[voi]
])
for voi in voi_indices
}
if variable_num_tol is not None:
up_bound = int(np.math.ceil(1 / variable_num_tol))
variable_num_tol = 1 / up_bound
cat = 'Integer'
else:
up_bound = 1
cat = 'Continuous'
if threshold is not None:
if b == c:
raise ValueError('dist_matrices not shaped correctly')
temp_dist_matrices = np.zeros((n_voi, n, J))
for i, voi in enumerate(voi_indices):
temp_ranks = evaluate_best_vertices(dist_matrices[i], np.arange(J),
S_sets[i])
temp_dist_matrices[i] = edit_dist_matrices(dist_matrices[i],
S_sets[i], temp_ranks,
threshold)
if api == 'pulp':
model = pulp.LpProblem(sense=pulp.LpMinimize)
inds = pulp.LpVariable.dicts("indicators for elements not in S",
('voi' + str(voi) + str(q)
for voi in voi_indices
for q in voi_to_Q_indices[voi]),
cat='Integer',
upBound=1,
lowBound=0)
weights = pulp.LpVariable.dicts("weights for representations",
(j for j in range(J)),
cat=cat,
upBound=up_bound,
lowBound=0)
model += (pulp.lpSum([
inds[('voi' + str(voi) + str(q))] for voi in voi_indices
for q in voi_to_Q_indices[voi]
]))
model += (pulp.lpSum([weights[(j)] for j in range(J)]) == up_bound)
for i, voi in enumerate(voi_indices):
dist_matrix = dist_matrices[i, :, :]
for s in voi_ind_to_S_sets[voi]:
for q in voi_to_Q_indices[voi]:
model += (pulp.lpSum([
weights[(j)] * dist_matrix[s, j] for j in range(J)
]) <= pulp.lpSum(
[weights[(j)] * dist_matrix[q, j]
for j in range(J)]) + pulp.lpSum(inds[
('voi' + str(voi) + str(q))] * M * up_bound))
try:
if solver == 'pulp':
model.solve()
elif solver == 'coin_cmd':
model.solve(solver=pulp.COIN_CMD())
alpha_hat = np.array([w.varValue for w in weights.values()])
except Exception as e:
print(e)
return None
elif api == 'py-mip':
model = mip.Model(sense=mip.MINIMIZE)
# Need to fix inds
inds = [[
model.add_var(name='inds', var_type=mip.BINARY)
for q in voi_to_Q_indices[voi]
] for voi in voi_indices]
# if variable_num_tol is None:
weights = [
model.add_var(name='weights', lb=0.0, ub=up_bound, var_type=cat)
for j in range(J)
]
model += mip.xsum(w for w in weights) == up_bound
# else:
# weights = [model.add_var(name='weights', lb=0, ub=up_bound, var_type='I') for j in range(J)]
# model += mip.xsum(w for w in weights) == up_bound
for i, voi in enumerate(voi_indices):
dist_matrix = dist_matrices[i, :, :]
for s in voi_ind_to_S_sets[voi]:
for k, q in enumerate(voi_to_Q_indices[voi]):
model += mip.xsum(
weights[j] * dist_matrix[s, j]
for j in range(J)) <= mip.xsum(
weights[j] * dist_matrix[q, j]
for j in range(J)) + inds[i][k] * M * up_bound
model.objective = mip.xsum(mip.xsum(i for i in ind) for ind in inds)
model.optimize(max_seconds=max_seconds)
alpha_hat = np.array([w.x for w in weights])
if alpha_hat[0] is None:
return None
else:
return alpha_hat / np.sum(alpha_hat)
def combine_representations(dist_matrix,
voi_index,
S_indices,
return_new_dists=True,
threshold=None,
solver='coin_cmd',
api='py-mip',
variable_num_tol=0.001,
max_seconds=np.inf):
"""
A function to find the weights of optimal linear combination of representations.
Input
dist_matrix - np.array (shape=(n, J))
Array containing the distances between the vertex of interest and the other n - 1
vertices.
voi_index - int
Index of vertex of interest.
S_indices - array-like
Indices of the vertices that should be at the top of the
nomination list for the vertex of interest.
return_new_dists - bool
If true, returns both the weights and the corresponding distance matrix.
threshold - maximum value of the rank of an element of S_indices.
solver - solver to use. in {'coin_cmd'}
api - api to use. in {'gurobi', 'py-mip', 'pulp'}
variable_num_tol - float in (0, 1]. resolution of the approximation of the continuous weights.
If None, continuous weights are found.
max_seconds - float in (0, inf)
Return
weights - np.array (length=J)
Array containing the coefficients for the optimal distance function.
-- optional -- new_dists - np.array (length=n)
Array containing the distances after applying the learned weight vector.
"""
# Grab the shape of the data that was passed int
n, J = dist_matrix.shape
# Pre-process the data so that there are no elements of S_indices that are above threshold
if threshold is not None:
ranks = evaluate_best_vertices(dist_matrix,
vertices=np.arange(J),
s_star=S_indices)
dist_matrix = edit_dist_matrices(dist_matrix, S_indices, ranks,
threshold)
# Grab the maximum value of dist_matrix (to be used as an upper bound later)
M = np.max(abs(dist_matrix))
# Grab the number of elements known to be similar to voi_index
S = len(S_indices)
# Define an array of integers corresponding to elements not in S_indices
Q_indices = np.array([
int(i)
for i in np.concatenate((range(0, voi_index), range(voi_index + 1, n)))
if i not in S_indices
])
# Grab the number of elements not known to be similar to voi_index
Q = len(Q_indices)
# We can either use continuous weights for the representations or use discrete approximation
if variable_num_tol is not None:
# Here, we are using a discrete approximation
# variable_num_tol is in the interval (0, 1]. If variable_num_tol is close to 0 that means
# we want our approximation to be of a high resolution. To achieve this, we define 1 / variable_num_tol
# to be the maximum value that a particular weight can take on.
# i.e. with variable_num_tol = 0.1, the weights can take on values 0.0, 0.1, 0.2, 0.3, .., 1.
# We normalize later.
up_bound = int(np.math.ceil(1 / variable_num_tol))
variable_num_tol = 1 / up_bound
cat = 'Integer'
else:
# Here, we let the weights be continuous
up_bound = 1
cat = 'Continuous'
# We've implemented the ILP in 3 different APIs: pulp, py-mip and gurobi.
# Gurobi seems to be the industry standard but licensing is a bit prohibitive.
# Each API is relatively similar. First, you define a set of variables.
# Then, using these variables, you define an objective function and a set of constraints that you assign to a model object.
if api == 'pulp':
# Define a model.
model = pulp.LpProblem(sense=pulp.LpMinimize)
# Define indicator variables (as defined in section 1.2 of https://arxiv.org/pdf/2005.10700.pdf) for every vertex
# That is not in S_indices.
# NB: i am more than happy to walk through the paper if that would be helpful!
inds = pulp.LpVariable.dicts("indicators for elements not in S",
(q for q in Q_indices),
cat='Integer',
upBound=1,
lowBound=0)
# Define non-negative weight variables for each of the representations.
weights = pulp.LpVariable.dicts("weights for representations",
(j for j in range(J)),
cat=cat,
upBound=up_bound,
lowBound=0)
# Set the objective function.
model += (pulp.lpSum([inds[(q)] for q in Q_indices]))
# Add constraint that the weights must sum to the upper bound defined by variable_num_tol.
model += (pulp.lpSum([weights[(j)] for j in range(J)]) == up_bound)
# Add constraint that elements of S_indices should be closer than elements not in S_indices (or, the Q_indices)
for s in S_indices:
for q in Q_indices:
model += (pulp.lpSum(
[weights[(j)] * dist_matrix[s, j]
for j in range(J)]) <= pulp.lpSum(
[weights[(j)] * dist_matrix[q, j]
for j in range(J)]) +
pulp.lpSum(inds[(q)] * M * up_bound))
# Different solvers for api == 'pulp'
try:
if solver == 'pulp':
model.solve()
elif solver == 'coin_cmd':
model.solve(solver=pulp.COIN_CMD())
alpha_hat = np.array([w.varValue for w in weights.values()])
except Exception as e:
print(e)
return None
alpha_hat = np.array([w.varValue for w in weights.values()])
elif api == 'gurobi':
model = gp.Model()
model.setParam('OutputFlag', 0)
ind = model.addVars(Q, vtype=GRB.BINARY, name='ind')
model.setObjective(gp.quicksum(ind), GRB.MINIMIZE)
w = model.addVars(J, lb=0, ub=1, vtype=GRB.CONTINUOUS, name='w')
model.addConstr(w.sum() == 1)
for s in S_indices:
temp_s = gp.tupledict([((i), dist_matrix[s, i]) for i in range(J)])
for i, q in enumerate(Q_indices):
temp_q = gp.tupledict([((i), dist_matrix[q, i])
for i in range(J)])
model.addConstr(w.prod(temp_s) <= w.prod(temp_q) + ind[i] * M)
model.optimize()
alpha_hat = np.array([i.X for i in list(w.values())])
elif api == 'py-mip':
model = mip.Model(sense=mip.MINIMIZE)
inds = [
model.add_var(name='inds', var_type=mip.BINARY) for q in range(Q)
]
# if variable_num_tol is None:
weights = [
model.add_var(name='weights', lb=0.0, ub=up_bound, var_type=cat)
for j in range(J)
]
model += mip.xsum(w for w in weights) == 1
# else:
# weights = [model.add_var(name='weights', lb=0, ub=up_bound, var_type='I') for j in range(J)]
# model += mip.xsum(w for w in weights) == up_bound
for s in S_indices:
for i, q in enumerate(Q_indices):
model += mip.xsum(
weights[j] * dist_matrix[s, j]
for j in range(J)) <= mip.xsum(
weights[j] * dist_matrix[q, j]
for j in range(J)) + inds[i] * M * up_bound
model.objective = mip.xsum(ind for ind in inds)
model.optimize(max_seconds=max_seconds)
alpha_hat = np.array([w.x for w in weights])
else:
raise ValueError("api %s not implemented" % (api))
# Return the new distances if told to do so.
if return_new_dists:
if np.sum(alpha_hat == 0) == J:
return alpha_hat, dist_matrix
else:
return alpha_hat, np.average(dist_matrix,
axis=1,
weights=alpha_hat)
if alpha_hat[0] is None:
return None
else:
# Normalize
return alpha_hat / np.sum(alpha_hat)
def get_all_tautomers(
molecule: 'Molecule',
total_number_hydrogens: Optional[int] = None,
net_charge: Optional[int] = None,
enforce_octet_rule: bool = True,
allow_radicals: bool = False,
max_tautomers: Optional[int] = 2000,
disallow_triple_bond_in_small_rings: bool = True,
disallow_allenes_in_small_rings: bool = True,
disallow_allenes_completely: bool = True,
lock_phenyl_rings: bool = True,
use_gurobi: bool = False,
maximum_number_hydrogens_per_atom: Optional[int] = 3,
skeletal_rearrangements: Optional[List[Tuple[int, int]]] = None,
debug: Optional[TextIO] = None,
) -> 'Molecule':
'''
Args:
``disallow_triple_bond_in_small_rings``: Disallow triple bonds in small rings (rings with size <= ``SMALL_RING``).
``disallow_allenes_in_small_rings``: Disallow allenes (=C=) in small rings (rings with size <= ``SMALL_RING``).
``lock_phenyl_rings``: Prevent phenyl rings (6 membered rings with all sp3 carbons) from being hydrogenised.
``disallow_allenes_completely``: Disallow allenes (=C=) completely.
``maximum_number_hydrogens_per_atom``: Maximum number of hydrogen atoms carried by a single heavy atom. Single atom molecules will be allowed ``maximum_number_hydrogens_per_atom + 1`` hydrogens.
``skeletal_rearrangements``: Optional list of bonds (tuple of two atom indices) that can potentially be formed or destroyed during the tautomerisation process.
Returns:
A list of tautomers (Molecule).
'''
print_if_debug = lambda *args: write_to_debug(debug, *args)
try:
optional_bonds = set() if skeletal_rearrangements is None else {
frozenset([atom_1, atom_2])
for (atom_1, atom_2) in skeletal_rearrangements
}
except:
raise TypeError(
'Invalid skeletal_rearrangements: {0}. Example: [(1, 2), (1, 3)]'.
format(skeletal_rearrangements))
molecule.bonds |= optional_bonds
if len([1 for atom in molecule.atoms.values() if atom.element == 'H']) > 0:
molecule.remove_all_hydrogens(mark_all_uncapped=True)
neighbour_counts = molecule.neighbours_for_atoms()
def keep_capping_strategy_for_atom(capping_strategy: Capping_Strategy,
atom: Atom) -> bool:
if atom.valence is not None:
if False:
if neighbour_counts[
atom.index] + new_atom_for_capping_strategy(
capping_strategy) == atom.valence:
print_if_debug(atom, capping_strategy)
return neighbour_counts[
atom.index] + new_atom_for_capping_strategy(
capping_strategy) == atom.valence
else:
return min_valence_for(atom) <= neighbour_counts[
atom.index] + new_atom_for_capping_strategy(
capping_strategy) <= max_valence_for(atom)
assert maximum_number_hydrogens_per_atom >= 0, 'Maximum number of hydrogens should be greater or equal than 0'
def possible_capping_strategies_for_atom(
atom: Atom,
is_phenyl_atom: bool = False) -> List[Capping_Strategy]:
if is_phenyl_atom and lock_phenyl_rings:
return [NO_CAP, H_CAP]
else:
return ([NO_CAP] + [
merge_caps(*[H_CAP] * i) for i in range(
1, maximum_number_hydrogens_per_atom + 1 +
(1 if len(molecule.atoms) == 1 else 0))
])
atoms_need_capping = [
atom for atom in molecule.sorted_atoms() if not atom.capped
]
print_if_debug([
possible_capping_strategies_for_atom(
atom, is_phenyl_atom=(atom.index in molecule.phenyl_atoms))
for atom in atoms_need_capping
], )
problem = LpProblem(
"Tautomer enumeration problem for molecule {0}".format(molecule.name),
LpMinimize)
ELECTRON_MULTIPLIER = (2 if not allow_radicals else 1)
non_allene_atoms = {}
for atom in molecule.atoms.values():
if disallow_allenes_completely:
atom_bonds = [
bond for bond in molecule.bonds if atom.index in bond
]
if atom.element == 'C' and len(atom_bonds) == 2:
non_allene_atoms[atom] = atom_bonds
fragment_switches, fragment_scores, fragment_H_scores = {}, {}, {}
capping_atoms_for = {}
new_bonds_sets = {}
for uncapped_atom in atoms_need_capping:
possible_capping_strategies = possible_capping_strategies_for_atom(
uncapped_atom,
is_phenyl_atom=(uncapped_atom.index in molecule.phenyl_atoms))
if len(possible_capping_strategies) == 0 or len(
possible_capping_strategies
) == 1 and possible_capping_strategies[0] == NO_CAP:
pass
else:
for (i, capping_strategy) in enumerate(
sorted(possible_capping_strategies), start=1):
print_if_debug(uncapped_atom, capping_strategy, i)
# Add switch variable
fragment_switches[uncapped_atom.index, i] = LpVariable(
'F_{i},{j}'.format(i=uncapped_atom.index, j=i),
0,
1,
LpBinary,
)
new_atoms, new_bonds = molecule.extend_molecule_with(
uncapped_atom, capping_strategy)
print_if_debug(i, [atom for atom in new_atoms])
capping_atoms_for[uncapped_atom.index, i] = new_atoms
new_bonds_sets[uncapped_atom.index, i] = [
bond for bond in new_bonds if uncapped_atom.index in bond
]
fragment_scores[uncapped_atom.index,
i] = len(capping_atoms_for[uncapped_atom.index,
i])
fragment_H_scores[uncapped_atom.index, i] = len([
atom for atom in capping_atoms_for[uncapped_atom.index, i]
if atom.element == 'H'
])
# Only choose one capping strategy at a time
problem += (lpSum(F_i for ((atom_id, _),
F_i) in fragment_switches.items()
if atom_id == uncapped_atom.index) == 1,
'Single capping strategy for atom {atom_desc}'.format(
atom_desc=atom_short_desc(uncapped_atom)))
all_capping_atoms = {
atom
for atoms in capping_atoms_for.values() for atom in atoms
}
all_capping_atom_ids = {atom.index for atom in all_capping_atoms}
non_capping_atoms = [
atom for atom in molecule.atoms.values()
if atom.index not in all_capping_atom_ids
]
fragment_switch_for_bond = {
bond: fragment_switch
for ((uncapped_atom_id, fragment_id),
fragment_switch) in fragment_switches.items()
for bond in new_bonds_sets[uncapped_atom_id, fragment_id]
}
by_switch_name = lambda T: T[1].name
get_bond = lambda T: T[0]
bonds_for_fragment_switch = {
key: [get_bond(T) for T in group]
for (key, group) in chain(
groupby(
sorted(
fragment_switch_for_bond.items(),
key=by_switch_name,
),
key=by_switch_name,
),
# Switches with no bonds, which are being "ignored" by groupby
[(fragment_switches[uncapped_atom_id, i].name, [])
for ((uncapped_atom_id, i), bonds) in new_bonds_sets.items()
if len(bonds) == 0])
}
non_capping_bonds = {
bond
for bond in molecule.bonds if len(bond & all_capping_atom_ids) == 0
}
if True:
molecule.write_graph('debug')
charges = {
atom.index:
LpVariable("C_{i}".format(i=atom.index), -MAX_ABSOLUTE_CHARGE,
MAX_ABSOLUTE_CHARGE, LpInteger)
for atom in non_capping_atoms
}
original_charges = list(charges.values())
# Extra variable use to bind charges
absolute_charges = {
atom_id: LpVariable("Z_{i}".format(i=atom_id), MIN_ABSOLUTE_CHARGE,
MAX_ABSOLUTE_CHARGE, LpInteger)
for atom_id in charges.keys()
}
non_bonded_electrons = {
atom_id:
LpVariable("N_{i}".format(i=atom_id), 0,
MAX_NONBONDED_ELECTRONS // ELECTRON_MULTIPLIER, LpInteger)
for (atom_id, atom) in molecule.atoms.items()
}
# Maps a bond to an integer
bond_mapping = {bond: i for (i, bond) in enumerate(molecule.bonds)}
# Maps an integer to a bond
bond_reverse_mapping = {v: k for (k, v) in bond_mapping.items()}
bond_key = lambda bond: ','.join(map(str, sorted(bond)))
if disallow_triple_bond_in_small_rings:
print_if_debug(
'Note: Excluding triple bonds in small rings (<= {0})'.format(
SMALL_RING))
bonds_in_small_rings = bonds_in_small_rings_for(molecule)
else:
bonds_in_small_rings = set()
bond_orders = {
bond: LpVariable(
"B_{bond_key}".format(bond_key=bond_key(bond)),
MIN_BOND_ORDER if bond not in optional_bonds else 0,
MAX_BOND_ORDER if bond not in bonds_in_small_rings else 2,
LpInteger,
) if bond not in fragment_switch_for_bond else
fragment_switch_for_bond[bond]
for bond in molecule.bonds
}
OBJECTIVES = [
MIN(lpSum(absolute_charges.values())),
]
H_size = lpSum([
F_i * fragment_H_scores[uncapped_atom_id, i]
for ((uncapped_atom_id, i), F_i) in fragment_switches.items()
])
if total_number_hydrogens is not None:
problem += H_size == total_number_hydrogens, 'Total number of hydrogens={0}'.format(
total_number_hydrogens)
total_size_objective = MIN(
lpSum([
F_i * fragment_scores[uncapped_atom_id, i]
for ((uncapped_atom_id, i), F_i) in fragment_switches.items()
]))
if sum([
fragment_scores[uncapped_atom_id, i]
for ((uncapped_atom_id, i), F_i) in fragment_switches.items()
]) != 0:
OBJECTIVES.append(total_size_objective)
OBJECTIVES.extend([
MIN(
lpSum([
charge * ELECTRONEGATIVITIES[molecule.atoms[atom_id].element]
for (atom_id, charge) in charges.items()
])),
MIN(
lpSum([
bond_order *
ELECTRONEGATIVITIES[molecule.atoms[atom_id].element]
for (bond, bond_order) in bond_orders.items()
for atom_id in bond
])),
])
if net_charge is not None:
problem += (lpSum(charges.values()) == net_charge,
'Known net charge={0}'.format(net_charge))
for atom in non_capping_atoms:
problem += (
charges[atom.index] == VALENCE_ELECTRONS[atom.element] - lpSum([
bond_orders[bond]
for bond in molecule.bonds if atom.index in bond
]) - ELECTRON_MULTIPLIER * non_bonded_electrons[atom.index],
'Electron balance for atom {element}_{index}'.format(
element=atom.element, index=atom.index),
)
# Deal with absolute values
for atom in non_capping_atoms:
problem += charges[atom.index] <= absolute_charges[
atom.index], 'Absolute charge contraint left {i}'.format(
i=atom.index)
problem += -charges[atom.index] <= absolute_charges[
atom.index], 'Absolute charge contraint right {i}'.format(
i=atom.index)
if enforce_octet_rule and atom not in all_capping_atoms:
if atom.element not in {'B', 'BE', 'P', 'S'}:
problem += (
ELECTRONS_PER_BOND * lpSum([
bond_orders[bond]
for bond in molecule.bonds if atom.index in bond
]) +
ELECTRON_MULTIPLIER * non_bonded_electrons[atom.index] == (
2 if atom.element in {'H', 'HE'} else 8),
'Octet for atom {element}_{index}'.format(
element=atom.element, index=atom.index),
)
for (atom, (bond_1, bond_2)) in non_allene_atoms.items():
new_allene_switch = LpVariable('A_{i}'.format(i=atom.index), 0, 1,
LpBinary)
problem += 2 * bond_orders[bond_1] - bond_orders[
bond_2] + 4 * new_allene_switch >= 3
problem += 2 * bond_orders[bond_1] - bond_orders[
bond_2] + 4 * new_allene_switch <= 5
for atom in atoms_in_small_rings_for(molecule):
if disallow_allenes_in_small_rings:
if atom.element in {'C', 'N'}:
adjacent_non_hydrogen_bonds = [
bond for bond in non_capping_bonds if atom.index in bond
]
if len(adjacent_non_hydrogen_bonds) == 2:
problem += sum(
bond_orders[bond]
for bond in adjacent_non_hydrogen_bonds
) <= 3, 'No allenes for atom {atom_desc} in short ring'.format(
atom_desc=atom_short_desc(atom))
def debug_failed_ILP(n: Optional[int] = None) -> None:
debug_filename = 'debug{0}.lp'.format(
'' if n is None else '_{n}'.format(n=n))
problem.writeLP(debug_filename)
print('Failed LP written to "{0}"'.format(debug_filename))
def encode_solution() -> int:
# bitshift is faster than multiplication by 2**i
return sum(
int(v.varValue) << i
for i, v in enumerate(fragment_switches.values()))
def new_molecule_for_current_solution(
n: Optional[int] = None) -> 'Molecule':
new_molecule = deepcopy(molecule)
if n is not None:
new_molecule.name += '_tautomer_{0}'.format(n)
DELETE_FAILED_CAPS = True
new_molecule.formal_charges, new_molecule.bond_orders, new_molecule.non_bonded_electrons = {}, {}, {}
atoms_to_remove = set()
for v in problem.variables():
variable_type, variable_substr = v.name.split('_')
if variable_type == 'C':
atom_index = int(variable_substr)
new_molecule.formal_charges[atom_index] = MUST_BE_INT(
v.varValue)
elif variable_type == 'B':
if False:
bond_index = int(variable_substr)
new_molecule.bond_orders[
bond_reverse_mapping[bond_index]] = MUST_BE_INT(
v.varValue)
else:
bond, bond_order = frozenset(
map(int, variable_substr.split(','))), MUST_BE_INT(
v.varValue)
if bond in optional_bonds:
if bond_order > 0:
print_if_debug('new_bond', bond, bond_order, [
new_molecule.atoms[atom_index]
for atom_index in bond
])
new_molecule.bond_orders[bond] = bond_order
else:
print_if_debug('no_new_bond', bond, bond_order, [
new_molecule.atoms[atom_index]
for atom_index in bond
])
new_molecule.bonds.remove(bond)
else:
if bond_order == 0:
print_if_debug('removing_bond', bond, bond_order, [
new_molecule.atoms[atom_index]
for atom_index in bond
])
new_molecule.bonds.remove(bond)
else:
new_molecule.bond_orders[bond] = bond_order
elif variable_type == 'Z':
pass
elif variable_type == 'A':
pass
elif variable_type == 'N':
atom_index = int(variable_substr)
new_molecule.non_bonded_electrons[atom_index] = MUST_BE_INT(
v.varValue) * ELECTRON_MULTIPLIER
elif variable_type == 'F':
uncapped_atom_id, capping_strategy_id = map(
int, variable_substr.split(','))
if MUST_BE_INT(v.varValue) == 0 and DELETE_FAILED_CAPS:
atoms_to_remove.add(
(uncapped_atom_id, capping_strategy_id))
for bond in bonds_for_fragment_switch[v.name]:
new_molecule.bond_orders[bond] = MUST_BE_INT(v.varValue)
elif variable_type == 'S':
capping_atom_id = int(variable_substr)
else:
raise Exception(
'Unknown variable type: {0}'.format(variable_type))
for atom_index in all_capping_atom_ids:
# Manually add electronic properties of hydrogens: charge=0, non_bonded_electrons=0
new_molecule.formal_charges[atom_index] = 0
new_molecule.non_bonded_electrons[atom_index] = 0
if DELETE_FAILED_CAPS:
for (uncapped_atom_id, capping_strategy_id) in atoms_to_remove:
new_molecule.remove_atoms(
atom for atom in capping_atoms_for[uncapped_atom_id,
capping_strategy_id])
if not allow_radicals and False:
assert all([
nonbonded_electrons % 2 == 0 for nonbonded_electrons in
new_molecule.non_bonded_electrons.values()
]), {
new_molecule.atoms[atom_index]: electrons
for (atom_index,
electrons) in new_molecule.non_bonded_electrons.items()
if electrons % 2 == 1
}
new_molecule.update_valences()
new_molecule.assign_aromatic_bonds()
new_molecule.assert_molecule_coherence()
return new_molecule
# Solve once to find optimal solution with lowest encode_solution()
try:
problem.sequentialSolve(OBJECTIVES, timeout=ILP_SOLVER_TIMEOUT)
assert problem.status == 1, (molecule.name, LpStatus[problem.status])
except Exception as e:
debug_failed_ILP(0)
raise
if debug is not None:
debug_failed_ILP(0)
all_tautomers = [new_molecule_for_current_solution(n=0)]
# Remove redundant constraints from previous multi-objective optimistion
for constraint_name in [
'1_Sequence_Objective', '2_Sequence_Objective',
'3_Sequence_Objective'
]:
del problem.constraints[constraint_name]
solutions = []
# Iterate until no more tautomers are found
for n in (count(1) if max_tautomers is None else range(1, max_tautomers)):
# exclude current optimal solution F*: \sum_{i}|F_i - F*_i| >= 1
problem += \
sum(v for v in fragment_switches.values() if not v.varValue) + \
sum(1 - v for v in fragment_switches.values() if v.varValue) >= 1,\
'Solution {n}'.format(n=n)
s = encode_solution()
print_if_debug('Excluding solution {0}'.format(s))
solutions.append(s)
try:
problem.solve(
solver=GUROBI_CMD() if use_gurobi else None,
timeout=ILP_SOLVER_TIMEOUT,
)
except Exception as e:
debug_failed_ILP(n)
raise
if problem.status == 1:
pass
else:
print_if_debug(problem.status, LpStatus[problem.status])
if debug is not None:
debug_failed_ILP(n)
break
all_tautomers.append(new_molecule_for_current_solution(n=n))
# check that all solutions are unique
assert len(solutions) == len(set(solutions))
return unique_molecules(all_tautomers)
def optimizeTrajectory(N, T, T_end, V, P, W, M, m, TSFC, minApproachDist, x_ini, x_fin, x_lim, u_lim, objects, r):
# --------------------
# Calculated Variables
# --------------------
del_t = T_end/T
numObjects = objects.shape[0]
# ------------------
# Decision variables
# ------------------
# Thrust
u = pulp.LpVariable.dicts(
"input", ((i, p, n) for i in range(T) for p in range(V) for n in range(N)),
cat='Continuous')
# Thrust Magnitude
v = pulp.LpVariable.dicts(
"inputMag", ((i, p, n) for i in range(T) for p in range(V) for n in range(N)),
lowBound=0,
cat='Continuous')
# State
x = pulp.LpVariable.dicts(
"state", ((i, p, k) for i in range(T) for p in range(V) for k in range(2*N)),
cat='Continuous')
# Object collision
a = pulp.LpVariable.dicts(
"objCol", ((i, p, l, k) for i in range(T) for p in range(V) for l in range(numObjects) for k in range(2*N)),
cat='Binary')
# Satellite Collision Avoidance
b = pulp.LpVariable.dicts(
"satelliteCollisionAvoidance", ((i, p, q, k) for i in range(T) for p in range(V) for q in range(V) for k in range(2*N)),
cat='Binary')
# Plume Impingement
c_plus = pulp.LpVariable.dicts(
"plumeImpingementPositive", ((i, p, q, n, k) for i in range(T) for p in range(V) for q in range(V) for n in range(N) for k in range(2*N)),
cat='Binary')
c_minus = pulp.LpVariable.dicts(
"plumeImpingementNegative", ((i, p, q, n, k) for i in range(T) for p in range(V) for q in range(V) for n in range(N) for k in range(2*N)),
cat='Binary')
# ------------------
# Optimization Model
# ------------------
# Instantiate Model
model = pulp.LpProblem("Satellite Fuel Minimization Problem", pulp.LpMinimize)
# Objective Function
model += pulp.lpSum(v[i, p, n] for i in range(T) for p in range(V) for n in range(N)), "Fuel Minimization"
# -----------
# Constraints
# -----------
# Basic Constraints
# -----------------
# Constrain thrust magnitude to abs(u[i, p, n])
for i in range(T):
for p in range(V):
for n in range(N):
model += u[i, p, n] <= v[i, p, n]
model += -u[i, p, n] <= v[i, p, n]
# State and Input vector start and end values
for p in range(V):
for k in range(2*N):
model += x[0, p, k] == x_ini[p, k]
model += x[T-1, p, k] == x_fin[p, k]
# Model Dynamics
for constraint in freeSpaceDynamics(x, u, T, V, N, m, del_t):
model += constraint
# State and Input vector limits
for i in range(T):
for p in range(V):
for n in range(N):
model += u[i, p, n] <= u_lim[p, n]
model += u[i, p, n] >= -u_lim[p, n]
for k in range(2*N): # Necessary?
model += x[i, p, k] <= x_lim[p, k]
model += x[i, p, k] >= -x_lim[p, k]
# Obstacle Avoidance
# ------------------
if objects.shape[1] > 0:
for i in range(T):
for p in range(V):
for l in range(numObjects):
model += pulp.lpSum(a[i, p, l, n] for n in range(2*N)) <= 2*N-1
for n in range(N):
model += x[i, p, n] >= objects[l, N+n] + minApproachDist - M*a[i, p, l, N+n]
model += x[i, p, n] <= objects[l, n] - minApproachDist + M*a[i, p, l, n]
# Collision Avoidance
# -------------------
if V > 1: # If more than one vehicle
for i in range(T):
for p in range(V):
for q in range(V):
if q > p:
model += pulp.lpSum(b[i, p, q, k] for k in range(2*N)) <= 2*N-1
for n in range(N):
model += x[i, p, n] - x[i, q, n] >= r[n] - M*b[i, p, q, n]
model += x[i, q, n] - x[i, p, n] >= r[n] - M*b[i, p, q, n+N]
# Plume Impingement
# -----------------
# Positive thrust
if V > 1: # If more than one vehicle
for i in range(T):
for p in range(V):
for q in range(V):
if q != p:
for n in range(N):
model += pulp.lpSum(c_plus[i, p, q, n, k] for k in range(2*N)) <= 2*N
model += -u[i, p, n] >= - M*c_plus[i, p, q, n, 0]
model += x[i, p, n] - x[i, q, n] >= P - M*c_plus[i, p, q, n, n]
model += x[i, q, n] - x[i, p, n] >= - M*c_plus[i, p, q, n, n+N]
for m in range(N):
if m != n:
x[i, p, m] - x[i, q, m] >= W - M*c_plus[i, p, q, n, m]
x[i, q, m] - x[i, p, m] >= W - M*c_plus[i, p, q, n, m+N]
# Negative thrust
for i in range(T):
for p in range(V):
for q in range(V):
if q != p:
for n in range(N):
model += pulp.lpSum(c_minus[i, p, q, n, k] for k in range(2*N)) <= 2*N
model += u[i, p, n] >= - M*c_minus[i, p, q, n, 0]
model += x[i, p, n] - x[i, q, n] >= - M*c_minus[i, p, q, n, n]
model += x[i, q, n] - x[i, p, n] >= P - M*c_minus[i, p, q, n, n+N]
for m in range(N):
if m != n:
x[i, p, m] - x[i, q, n] >= W - M*c_minus[i, p, q, n, m]
x[i, q, m] - x[i, p, m] >= W - M*c_minus[i, p, q, n, m+N]
# Plume Avoidance for Vehicles
# ----------------------------
# Plume Avoidance for Obstacles
# -----------------------------
# Final Configuration Selection
# -----------------------------
# Solve model and return results in dictionary
model.solve(pulp.CPLEX())
# Create Pandas dataframe for results and return
return {'model':model, 'x':x, 'u':u}
scene_sizes = [len(scene_indices[scenes[i]]) for i in range(0, len(scenes))]
# Matrix on same format as decision variable, R[i, j] = 1 if actor j can play role i
R = {}
for i in range(0, m):
for j in range(0, n):
R[i, j] = int(actors[j] in possible_actors[roles[i]])
## State binary LP
# Decision variable
x = LpVariable.dicts('Rollefordeling', [(i, j) for i in range(0, m)
for j in range(0, n)], 0, 1, LpBinary)
prob = LpProblem('RoleDistribution', LpMaximize)
# Maximize number of people who have a role
prob += lpSum(x[i, j] for i in range(0, m) for j in range(0, n))
# Only one role per person
for j in range(0, n):
prob += lpSum(x[i, j] for i in range(0, m)) <= 1
# Only one person per role
for i in range(0, m):
prob += lpSum(x[i, j] for j in range(0, n)) <= 1
# Each scene should either have all roles assigned, or none (meaning it is not included)
scene_included = LpVariable.dicts('SceneChoice', range(0, t), 0, 1, LpBinary)
for s in range(0, t):
current_scene_indices = scene_indices[scenes[s]]
prob += lpSum(x[i, j] for i in current_scene_indices
for j in range(0, n)) == scene_included[s] * scene_sizes[s]
def optimize(self, prices, forecasts=None, initial_charge=0, timestep='5min'):
"""Run the linear program to optimize the battery.
prices list [$/MWh]
forecasts list [$/MWh]
initial_charge float [MWh]
timestep str 5min, 1hr etc
"""
self.prob = LpProblem('cost minimization', LpMinimize)
self.timestep = timestep
timestep_timedelta = parse_timedelta(timestep)
timestep_hours = timestep_timedelta.total_seconds() / (60*60)
self.step = 1 / timestep_hours
# append a NaN onto the prices list to represent the price
# during the last reported period, which is only used to give the
# final charge, and not included in the optimization
prices = list(prices)
prices.append(None)
if forecasts is None:
forecasts = prices
else:
# If we're not inheriting the prices, we need to append to forecast
# to match the price list.
forecasts.append(None)
forecast_len = len(forecasts)
price_len = len(prices)
len_msg = """
The number of forecasts({}) should match the number of prices({}).
""".format(forecast_len, price_len)
assert forecast_len == price_len, len_msg
assert initial_charge <= self.capacity
assert initial_charge >= 0
# used to index timesteps
idx = range(0, len(prices))
self.vars = self.setup_vars(idx)
imports = self.vars['imports']
exports = self.vars['exports']
charges = self.vars['charges']
losses = self.vars['losses']
# the objective function we are minimizing
self.prob += lpSum(
[imports[i] * forecasts[i] for i in idx[:-1]] +
[-(exports[i] - losses[i]) * forecasts[i] for i in idx[:-1]]
)
# initial charge
self.prob += charges[0] == initial_charge
# last item in the index isn't used because the last timestep only
# represents the final charge level - no import or export is done
for i in idx[:-1]:
# energy balance across two time periods
self.prob += charges[i + 1] == charges[i] + (imports[i] - exports[i]) / self.step
# constrain battery charge level
self.prob += charges[i] <= self.capacity
self.prob += charges[i] >= 0
self.prob += losses[i] == exports[i] * (1 - self.efficiency)
print('starting linear program for {}'.format(self))
self.prob.solve()
opt_results = {
"name": "optimization_results",
"status": LpStatus[self.prob.status]
}
print('linear program for {} done - {}'.format(self, opt_results['status']))
logger.info(json.dumps(opt_results))
self.info = self.generate_outputs(prices, forecasts, idx,
initial_charge)
return self.info
def sign(agent_id: str, agreements: List[Contract],
trust_probabilities: Dict[str, float], inventory: int):
"""
Given a list of agreements and trust probabilities, each of type negmas.Contract, decides which agreements to sign.
:param agent_id: the agent's id (self.id of the calling agent)
:param agreements: a list of agreements, each of type negmas.Contracts.
:param trust_probabilities: a dictionary mapping an agent's id to its trust probability
:return: a dictionary with information about the solver. In particular, the dictionary contains an entry 'list_of_signatures' which is
a list of the same length as the input list of agreements. The i-th element of the list 'list_of_signatures' is self.id/None in case
the agent wants/do not wants to sign the i-th agreement in the input list.
"""
# If the list of agreements is empty, then return an empty list of signatures.
if len(agreements) == 0:
return {
'list_of_signatures': [],
'agent_id': agent_id,
'model': None,
'time_to_generate_ilp': None,
'time_to_solve_ilp': None,
'agreements': agreements,
'trust_probabilities': trust_probabilities,
'profit': None
}
# Partition agreements into buy and sell agreements.
agreements_to_buy_inputs, agreements_to_sell_outputs = SCMLContractsSigner.partition_agreements(
agent_id, agreements, trust_probabilities)
# If there are no sell contracts, the signer has nothing to do and signs nothing.
if len(agreements_to_sell_outputs) == 0:
return {
'list_of_signatures': [None] * len(agreements),
'agent_id': agent_id,
'model': None,
'time_to_generate_ilp': None,
'time_to_solve_ilp': None,
'agreements': agreements,
'trust_probabilities': trust_probabilities,
'profit': None
}
# For efficiency purposes, we order the agreements by delivery times. But, before we do, we must be able to
# recover the indices of the agreements as given to the solver, otherwise, we can't map the output to the right agreements.
buy_agreements = [
agreement + (i, )
for i, agreement in enumerate(agreements_to_buy_inputs)
]
sell_agreements = [
agreement + (i, )
for i, agreement in enumerate(agreements_to_sell_outputs)
]
# At this point an agreement is a tuple: (MASTER_INDEX, QUANTITY, TIME, PRICE, SUB_INDEX). Now we order by TIME.
buy_agreements = sorted(buy_agreements,
key=lambda x: x[SCMLContractsSigner.TIME])
sell_agreements = sorted(sell_agreements,
key=lambda x: x[SCMLContractsSigner.TIME])
# The code that follows will change the agreement lists, so we make a copy of them for later reference.
buy_agreements_copy = buy_agreements.copy()
sell_agreements_copy = sell_agreements.copy()
t0 = time.time()
# Decision variables
buy_sign_vars = pulp.LpVariable.dicts(
'buy_sign', (i for i, _ in enumerate(buy_agreements)),
lowBound=0,
upBound=1,
cat='Integer')
sell_sign_vars = pulp.LpVariable.dicts(
'sell_sign', (i for i, _ in enumerate(sell_agreements)),
lowBound=0,
upBound=1,
cat='Integer')
# Generate the pulp problem.
model = pulp.LpProblem('Contract_Signer_Solver', pulp.LpMaximize)
# The objective function is profit, defined as revenue minus cost.
model += pulp.lpSum([
sell_agreements[i][SCMLContractsSigner.QUANTITY] *
sell_agreements[i][SCMLContractsSigner.PRICE] *
sell_agreements[i][SCMLContractsSigner.PARTNER_TRUST] *
sell_sign_vars[s[SCMLContractsSigner.SUB_INDEX]]
for i, s in enumerate(sell_agreements)
] + [
-1.0 * buy_agreements[i][SCMLContractsSigner.QUANTITY] *
buy_agreements[i][SCMLContractsSigner.PRICE] *
buy_agreements[i][SCMLContractsSigner.PARTNER_TRUST] *
buy_sign_vars[b[SCMLContractsSigner.SUB_INDEX]]
for i, b in enumerate(buy_agreements)
])
# Construct the constraints. The constraint model inventory feasibility, i.e., we don't commit to a sell unless we have enough outputs.
current_sell_time = sell_agreements[0][SCMLContractsSigner.TIME]
current_sell_time_sum = []
partial_sell_sum = []
partial_buy_sum = []
result = []
while len(sell_agreements) > 0:
s = sell_agreements.pop(0)
if current_sell_time == s[SCMLContractsSigner.TIME]:
current_sell_time_sum += [
sell_sign_vars[s[SCMLContractsSigner.SUB_INDEX]] *
s[SCMLContractsSigner.QUANTITY]
]
else:
partial_buy_sum += SCMLContractsSigner.constraints_generation_helper(
buy_agreements, buy_sign_vars, current_sell_time)
result += [(current_sell_time_sum.copy(),
partial_buy_sum.copy(), partial_sell_sum.copy())]
partial_sell_sum += current_sell_time_sum
current_sell_time = s[SCMLContractsSigner.TIME]
current_sell_time_sum = [
sell_sign_vars[s[SCMLContractsSigner.SUB_INDEX]] *
s[SCMLContractsSigner.QUANTITY]
]
partial_buy_sum += SCMLContractsSigner.constraints_generation_helper(
buy_agreements, buy_sign_vars, current_sell_time)
result += [(current_sell_time_sum.copy(), partial_buy_sum.copy(),
partial_sell_sum.copy())]
for left, middle, right in result:
model += sum(left) <= inventory + sum(middle) - sum(right)
# Measure the time taken to generate the ILP.
time_to_generate_ilp = time.time() - t0
# Solve the integer program and hide the output given by the solver.
t0_solve = time.time()
model.solve(pulp.PULP_CBC_CMD(msg=False))
time_to_solve_ilp = time.time() - t0_solve
# Record which contracts should be signed. We start by assuming no contracts will be signed.
list_of_signatures = [None] * len(agreements)
for agreement in buy_agreements_copy:
if buy_sign_vars[agreement[
SCMLContractsSigner.
SUB_INDEX]].varValue is not None and int(
buy_sign_vars[agreement[
SCMLContractsSigner.SUB_INDEX]].varValue) == 1:
list_of_signatures[agreement[
SCMLContractsSigner.MASTER_INDEX]] = agent_id
for agreement in sell_agreements_copy:
if sell_sign_vars[agreement[
SCMLContractsSigner.
SUB_INDEX]].varValue is not None and int(
sell_sign_vars[agreement[
SCMLContractsSigner.SUB_INDEX]].varValue) == 1:
list_of_signatures[agreement[
SCMLContractsSigner.MASTER_INDEX]] = agent_id
# Return multiple objects for inspection purposes. In production, we care about the list of sign contracts, 'list_of_signatures'.
return {
'list_of_signatures': list_of_signatures,
'agent_id': agent_id,
'model': model,
'time_to_generate_ilp': time_to_generate_ilp,
'time_to_solve_ilp': time_to_solve_ilp,
'agreements': agreements,
'trust_probabilities': trust_probabilities,
'profit': pulp.value(model.objective)
}
# AG: we can live without this constraint if we min function
# model += pulp.lpSum(xa) == pulp.lpSum(requirement_a.values())
# model += pulp.lpSum(xb) == pulp.lpSum(requirement_b)
# Constraint: positive inventory
inv_a = dict()
inv_b = dict()
for d in days:
inv_b[d] = accumulate(xb, d) - accumulate(requirement_b, d)
model += inv_b[d] >= 0, f"Pos.inv.B at day {d}"
inv_a[d] = accumulate(xa, d) - accumulate(requirement_a, d)
model += inv_a[d] >= 0, f"Pos.inv.A at day {d}"
# Target: min inventory
# AG: added requirement_b_to_a term as we need to scale the penalty for b by the proportion of a
model += pulp.lpSum(
inv_a.values()) + pulp.lpSum(inv_b.values()) * (requirement_b_to_a + 1)
feasibility = model.solve()
print("Status:", pulp.LpStatus[feasibility])
if feasibility == 1:
import pandas as pd
df = pd.DataFrame(
dict(
sales_b=purchases_b,
production_b=peek(xb),
inventory_b=peek(inv_b),
processing_a=values(requirement_a) - purchases_a,
sales_a=purchases_a,
requirement_a=values(requirement_a),
production_a=peek(xa),
def __calcW(self, goal=1, eps=0.00):
oidx = [i for i in range(self.M)]
Nsols = len(self.solutionsList)
# Create a gurobi model
prob = lp.LpProblem("max_mean", lp.LpMaximize)
# prob = mip.Model(sense=mip.MAXIMIZE, solver_name=mip.GRB)
# Creation of linear integer variables
w = list(
lp.LpVariable.dicts('w', oidx, lowBound=0,
cat='Continuous').values())
# w = list(prob.add_var(name='w', var_type=mip.CONTINUOUS, lb=0) for i in range(len(Ingredients)))
uR = list(lp.LpVariable.dicts('uR', oidx, cat='Continuous').values())
# uR = list()
kp = list(
lp.LpVariable.dicts('kp', list(range(Nsols)),
cat='Continuous').values())
# kp = list()
nu = list(
lp.LpVariable.dicts('nu', oidx, lowBound=0,
cat='Continuous').values())
# nu = list()
test = False
if test:
kpB = list(
lp.LpVariable.dicts('kpB',
list(range(Nsols)),
lowBound=0,
upBound=1,
cat='Continuous').values())
nuB = list(
lp.LpVariable.dicts('nuB',
oidx,
lowBound=0,
upBound=1,
cat='Continuous').values())
else:
kpB = list(
lp.LpVariable.dicts('kpB', list(range(Nsols)),
cat='Binary').values())
nuB = list(lp.LpVariable.dicts('nuB', oidx, cat='Binary').values())
v = lp.LpVariable('v', cat='Continuous')
mu = lp.LpVariable('mu', cat='Continuous')
# Inherent constraints of this problem
for value, sols in enumerate(self.solutionsList):
expr = lp.lpDot(self.__normw(sols.w), uR)
cons = self.__normf(sols.objs) @ self.__normw(sols.w)
prob += expr >= cons * (1 - eps)
for i in oidx:
expr = uR[i] - lp.lpSum([
kp[conN] * self.__normf(sols.objs)[i]
for conN, sols in enumerate(self.solutionsList)
]) - nu[i] + mu
prob += expr == 0
bigC = max(self.__normf(self.__globalU))
for conN, sols in enumerate(self.solutionsList):
# d, dvec = self.__calcD(sols)
expr = v - lp.lpDot(w, self.__normf(sols.objs))
prob += -expr >= 0 #mantem
prob += -expr <= kpB[conN] * bigC #mantem
prob += kp[conN] >= 0 #mantem
prob += kp[conN] <= (1 - kpB[conN]) #mantem
for i in oidx:
prob += uR[i] >= self.__normf(self.__globalL)[i]
for i in oidx:
prob += w[i] >= 0 #mantem
prob += w[i] <= nuB[i] #mantem
prob += nu[i] >= 0 #mantem
prob += nu[i] <= (1 - nuB[i]) * 2 * bigC #mantem
prob += lp.lpSum([w[i] for i in oidx]) == 1
prob += lp.lpSum([kp[i] for i in range(Nsols)]) == 1
prob += mu
# desigualdades válidas
prob += mu <= v
try:
rnd = np.array(
sorted([0] + [np.random.rand()
for i in range(self.M - 1)] + [1]))
w_ini = np.array([rnd[i + 1] - rnd[i] for i in range(self.__M)])
w_ini = w_ini / w_ini.sum()
for wi, wii in zip(w, w_ini):
wi.start = wii
grbs = lp.GUROBI(epgap=self.__mip_gap,
SolutionLimit=1,
msg=False,
OutputFlag=False,
Threads=1)
prob.solve(grbs)
if self.__goal != float('inf'):
grbs = lp.GUROBI(timeLimit=self.__time_limit,
epgap=self.__mip_gap,
SolutionLimit=MAXINT,
msg=False,
BestObjStop=self.__goal,
OutputFlag=False,
Threads=1)
else:
grbs = lp.GUROBI(timeLimit=self.__time_limit,
epgap=self.__mip_gap,
SolutionLimit=MAXINT,
msg=False,
OutputFlag=False,
Threads=1)
prob.solve(grbs)
except:
cbcs = lp.COIN_CMD(maxSeconds=self.__time_limit,
fracGap=self.__mip_gap,
threads=1)
prob.solve(cbcs, use_mps=False)
feasible = False if prob.status in [-1, -2] else True
if feasible:
w_ = np.array(
[lp.value(w[i]) if lp.value(w[i]) >= 0 else 0 for i in oidx])
w_ = w_ / w_.sum()
if self.__norm:
w_ = w_ / (self.__globalU - self.__globalL)
fobj = lp.value(prob.objective)
self.__w = np.array(w_)
self.__importance = fobj
else:
raise ('Non-feasible solution')
Csums = pulp.LpVariable.dicts("line_prod", (prod_line, orderList), 0, None,
pulp.LpInteger)
r = pulp.LpVariable.dicts("release", (prod_line, orderList), 0)
compD = pulp.LpVariable.dicts("compDate", (prod_line, orderList), 0)
#CLines=pulp.LpVariable("CLines",[j for j in line_date] ,0) #total amount of all lines by day
Cmax = pulp.LpVariable.dicts("maxDate", [i for i in orderList], 0)
#Create the 'prob' variable to contain the problem data
prob = pulp.LpProblem("The APS Problem", pulp.LpMaximize)
#prob=pulp.LpProblem("The APS Problem",pulp.LpMinimize)
#objective function： consider order priority and leadtime
#eps=1e-2
#lambda compD[l][o] if compD[l][o]<=len(plan_dates) else len(plan_dates)
#
prob+=pulp.lpSum([orderPool['priority'][o]*orderPool['order_type'][o]*\
adt.model_total_volume(order_spd[(order_spd['order_id']==o)&(order_spd['line_no']==l)][['day_process','num_by_day']],adt.prod_days(compD[l][o],len(plan_dates),r[l][o]),len(plan_dates))\
for o in orderList for l in prod_line])
#prob+=Cmax+eps*lpSum([r[j] for j in order_line])-eps*[compD[j] for j in order_line]
#The constraints
#1. every order has to be completed before due date
#2. relationships between release date and due date
#3. release date>=max(0,epst-plan_dates[0])
#4.fixed sum across day and lines equal total num of orders
for o in orderList:
for l in prod_line:
prob += compD[l][o] <= Cmax[o]
#prob+=compD[l][o] >=r[l][o]+Csums[l][o]*(process_days[o][l]/orderPool['order_num'][o])
prob += compD[l][o] >= r[l][o] + adt.process_csum(
Csums[l][o], order_spd[
def blocked_reactions_analysis(
database,
pulp_solver,
specific_bounds,
custom_flux_constraints,
excluded_reactions=None,
target_reactions_list=None,
logger=None):
"""
Perform flux variability analysis on the database,
based on the overall reaction equation of optstoic.
If a reaction cannot carry flux (i.e., -eps <= v(j) <= eps, where eps = 1e-8),
then the reaction is considered as a blocked reaction.
The blocked reactions are then eliminated from the database S matrix.
Next, the internal loops (excluding cofactors) are identified.
Then, optStoic analysis can be performed for pathway prospecting.
max/min v(j)
subject to:
sum(j, S(i,j) * v(j)) = 0, for all i
custom_flux_constraints
Note: The glycolysis study was done using the GAMS version of this code.
This is written in attempt to port find_blocked_reactions.gms from GAMS to Python,
as a part of effort to generalize optstoic analysis.
Args:
database (:obj:`BaseReactionDatabase`): The default reaction database
without blocked reactions/loops.
pulp_solver (TYPE): The solver for PuLP.
specific_bounds (dict): LB and UB for exchange reactions which defined the
overall design equations. E.g. {'Ex_glc': {'LB': -1, 'UB':-1}}
custom_flux_constraints (TYPE): The custom constraints that need to be
added to the model formulation.
excluded_reactions (None, optional): The list of reactions that are manually
selected to be excluded from optstoic solution.
target_reactions_list (None, optional): If provided, the blocked reaction analysis is performed
only on a subset of the reaction provided. If None, the blocked reaction analysis
will be performed on all reactions in the database. The excluded_reactions set
can be subtracted(e.g., set(database.reactions) - excluded_reactions), since
they are blocked reactions.
logger (:obj:`logging.logger`, optional): The logging instance
Returns:
TYPE: Description
Raises:
ValueError: Description
Deleted Parameters:
user_defined_export_rxns_Sji (dict): The list of export reactions that
need to be added to the model for metabolite exchange (i.e., any metabolite
that participate in the design equation)
"""
if logger is None:
logger = create_logger(
name="optstoicpy.script.database_preprocessing.blocked_reactions_analysis")
logger.warning(
"This process may take a long time to run. It is recommended to be run in a batch script.")
M = 1000
EPS = 1e-8
# Initialize variables
v = pulp.LpVariable.dicts("v", database.reactions,
lowBound=-M, upBound=M, cat='Continuous')
for j in database.reactions:
if database.rxntype[j] == 0:
# Forward irreversible
v[j].lowBound = 0
v[j].upBound = M
elif database.rxntype[j] == 1:
# Reversible
v[j].lowBound = -M
v[j].upBound = M
elif database.rxntype[j] == 2:
# Reverse irreversible
v[j].lowBound = -M
v[j].upBound = 0
elif database.rxntype[j] == 4:
v[j].lowBound = 0
v[j].upBound = 0
else:
raise ValueError("Reaction type for reaction %s is unknown." % j)
if excluded_reactions is not None:
for j in excluded_reactions:
v[j].lowBound = 0
v[j].upBound = 0
# Fix stoichiometry of source/sink metabolites
for j, bounds in specific_bounds.items():
v[j].lowBound = bounds['LB']
v[j].upBound = bounds['UB']
FVA_res = {}
blocked_reactions = []
lp_prob = None
if target_reactions_list is None:
target_reactions_list = database.reactions
num_rxn = len(target_reactions_list)
for ind, j1 in enumerate(target_reactions_list):
logger.debug("%s/%s" % (ind, num_rxn))
FVA_res[j1] = {}
for obj in ['min', 'max']:
# Variables (make a copy)
vt = copy.deepcopy(v)
del lp_prob
# Objective function
if obj == 'min':
lp_prob = pulp.LpProblem("FVA%s" % obj, pulp.LpMinimize)
lp_prob += vt[j1], "FVA_min"
elif obj == 'max':
lp_prob = pulp.LpProblem("FVA%s" % obj, pulp.LpMaximize)
lp_prob += vt[j1], "FVA_max"
# Constraints
# Mass_balance
for i in database.metabolites:
# If metabolites not involve in any reactions
if i not in database.S:
continue
label = "mass_balance_%s" % i
dot_S_v = pulp.lpSum([database.S[i][j] * vt[j]
for j in list(database.S[i].keys())])
condition = dot_S_v == 0
lp_prob += condition, label
if custom_flux_constraints is not None:
logger.info("Adding custom constraints...")
for group in custom_flux_constraints:
lp_prob += pulp.lpSum(vt[rxn] for rxn in group['reactions']
) <= group['UB'], "%s_UB" % group['constraint_name']
lp_prob += pulp.lpSum(vt[rxn] for rxn in group['reactions']
) >= group['LB'], "%s_LB" % group['constraint_name']
lp_prob.solve(solver=pulp_solver)
FVA_res[j1][obj] = pulp.value(lp_prob.objective)
if (FVA_res[j1]['max'] < EPS) and (FVA_res[j1]['min'] > -EPS):
blocked_reactions.append(j1)
json.dump(FVA_res,
open("temp_FVA_result.json", 'w+'),
sort_keys=True,
indent=4)
return blocked_reactions, FVA_res
def build_local_problem_lp(pb, name):
lp = pulp.LpProblem(name + ".lp", pulp.LpMinimize)
lp.setSolver()
prod_vars = {}
in_use_vars = {}
defaillance = {}
cout_proportionnel_defaillance = 3000 #euros/MWh
#demande = demande-auto_conso
#auto_conso<=Enr
#Enr=Enr-auto_conso
#inject=
for t in pb.time_steps:
prod_vars[t] = {}
in_use_vars[t] = {}
var_name = "battery_stock" + str(t)
prod_vars[t]["battery_stock"] = pulp.LpVariable(
var_name, 0.0, pb.battery.energy_max)
var_name = "battery_store" + str(t)
prod_vars[t]["battery_store"] = pulp.LpVariable(
var_name, -pb.battery.power_max_store, 0.0)
var_name = "battery_prod" + str(t)
prod_vars[t]["battery_prod"] = pulp.LpVariable(
var_name, 0.0, pb.battery.power_max_prod)
cnt_name = "cnt_storage_evolution_" + str(t)
if t == pb.time_steps[0]:
#initial stock=0
lp += prod_vars[t][
"battery_stock"] == pb.time_step_duration / 60.0 * (
-pb.battery.efficiency * prod_vars[t]["battery_store"] -
prod_vars[t]["battery_prod"]), cnt_name
else:
lp += prod_vars[t]["battery_stock"] == prod_vars[
t - 1]["battery_stock"] + pb.time_step_duration / 60.0 * (
-pb.battery.efficiency * prod_vars[t]["battery_store"] -
prod_vars[t]["battery_prod"]), cnt_name
var_name = "auto_conso_" + str(t)
prod_vars[t]["auto_conso"] = pulp.LpVariable(var_name, 0.0,
pb.local_demand[t])
var_name = "payed_" + str(t)
prod_vars[t]["payed_conso"] = pulp.LpVariable(var_name, 0.0,
pb.local_demand[t])
var_name = "sold_local_production" + str(t)
prod_vars[t]["sold_local_production"] = pulp.LpVariable(
var_name, 0.0, pb.local_solar_power[t] + pb.battery.power_max_prod)
lp += prod_vars[t]["auto_conso"] + prod_vars[t][
"payed_conso"] == pb.local_demand[t], "cnt_local_conso_" + str(t)
lp += prod_vars[t][
"sold_local_production"] == pb.local_solar_power[t] - prod_vars[t][
"auto_conso"] + prod_vars[t]["battery_prod"] + prod_vars[t][
"battery_store"], "cnt_local_solar_" + str(t)
lp.setObjective( pb.electricity_tariff*pulp.lpSum([prod_vars[t]["payed_conso"] for t in pb.time_steps])* pb.time_step_duration/60.0\
-pulp.lpSum([pb.solar_power_selling_price_chronicle[t]*prod_vars[t]["sold_local_production"] for t in pb.time_steps]) * pb.time_step_duration/60.0)
model = Model(lp, prod_vars)
return model
def run(total, price):
y = [None] * len(price)
names = [p[1] for p in price]
problem = pulp.LpProblem("Problem-1", pulp.LpMinimize)
for index in range(len(price)):
y[index] = pulp.LpVariable("y" + str(index), 0, 2, pulp.LpInteger)
# problem += pulp.lpSum([y[i] for i in range(len(price))]), "The number of items"
prices = [y[i] * price[i][0] for i in range(len(price))]
# prices = [y[i] * price[i] for i in range(len(price))]
problem += total - pulp.lpSum(prices), "Use as much"
problem += total == pulp.lpSum(prices), "Use exact"
problem += pulp.lpSum(prices) <= total, "Do not over"
for index in range(len(price)):
if len(price[index]) >= 4:
# Set minimum
problem += y[index] >= price[index][3]
if len(price[index]) >= 3:
# Set maximum
problem += y[index] <= price[index][2]
# 問題の式全部を表示
print("問題の式")
print("--------")
print(problem)
print("--------")
print(price)
# 計算
result_status = problem.solve()
# （解が得られていれば）目的関数値や解を表示
if pulp.LpStatus[result_status] != "Optimal":
print("解なし")
return {"ok": False}
else:
obj = pulp.value(problem.objective)
if obj != 0:
print("解無し 残額: %d" % (obj))
return {"ok": False}
else:
print("")
print("計算結果")
print("********")
print("最適性 = {}".format(pulp.LpStatus[result_status]))
print("目的関数値 = {}".format(obj))
print("y = {}".format([int(pulp.value(v)) for v in y]))
print("********")
print(" ", price)
used = 0
for i, v in enumerate(zip(price, [int(pulp.value(v)) for v in y])):
p, n = v
if n > 0:
print("{}\t{}\t{}\t{}".format(i, p[0], n, p[1]))
used += p[0] * n
print("Total: ", used)
count = {names[i]: int(pulp.value(y[i])) for i in range(len(y))}
return {"ok": True, "count": count}
missingMatching = False
print(len(matched))
# Create the model
model = LpProblem(name="small-problem", sense=LpMinimize)
# Initialize the decision variables
variables = [
LpVariable(name=f"{i}", lowBound=0, upBound=1)
for i in sorted(graph.keys())
]
for x in range(0, len(variables)):
for edge in graph[x]:
model += (variables[x - 1] + variables[edge - 1] >= 1)
# Add the objective function to the model
model += lpSum(variables)
status = model.solve()
print(f"status: {model.status}, {LpStatus[model.status]}")
print(f"objective: {model.objective.value()}")
# Solve the problem
status = model.solve()
for var in model.variables():
if var.value() >= 0.5:
var.value = lambda: 1
else:
var.value = lambda: 0
print(f"{var.name}: {var.value()}")
print(f"objective: {model.objective.value()}")
def cg_secp_ilp(
cg: ComputationConstraintsHyperGraph,
agents: List[AgentDef],
already_assigned: Distribution,
computation_memory: Callable[[ComputationNode], float],
communication_load: Callable[[ComputationNode, str], float],
) -> Distribution:
agents = list(agents)
agents_names = [a.name for a in agents]
# Only keep computations for which we actually need to find an agent.
comps_to_host = [
c for c in cg.node_names() if not already_assigned.has_computation(c)
]
# x_i^k : binary variable indicating if var x_i is hosted on agent a_k.
xs = _build_cs_binvar(comps_to_host, agents_names)
# alpha_ijk : binary variable indicating if x_i and f_j are both on a_k.
alphas = _build_alphaijk_binvars(cg, agents_names)
logger.debug(f"alpha_ijk {alphas}")
# LP problem with objective function (total communication cost).
pb = LpProblem("distribution", LpMinimize)
pb += (
_objective_function(cg, communication_load, alphas, agents_names),
"Communication costs",
)
# Constraints.
# All variable computations must be hosted:
for i in comps_to_host:
pb += (
lpSum([xs[(i, k)] for k in agents_names]) == 1,
"var {} is hosted".format(i),
)
# Each agent must host at least one computation:
# We only need this constraints for agents that do not already host a
# computation:
empty_agents = [
a for a in agents_names if not already_assigned.computations_hosted(a)
]
for k in empty_agents:
pb += (
lpSum([xs[(i, k)] for i in comps_to_host]) >= 1,
"atleastone {}".format(k),
)
# Memory capacity constraint for agents
for a in agents:
# Decrease capacity for already hosted computations
capacity = a.capacity - sum([
secp_computation_memory_in_cg(c, cg, computation_memory)
for c in already_assigned.computations_hosted(a.name)
])
pb += (
lpSum([
secp_computation_memory_in_cg(i, cg, computation_memory) *
xs[(i, a.name)] for i in comps_to_host
]) <= capacity,
"memory {}".format(a.name),
)
# Linearization constraints for alpha_ijk.
for (i, j), k in alphas:
if i in comps_to_host and j in comps_to_host:
pb += alphas[((i, j), k)] <= xs[(i, k)], "lin1 {}{}{}".format(
i, j, k)
pb += alphas[((i, j), k)] <= xs[(j, k)], "lin2 {}{}{}".format(
i, j, k)
pb += (
alphas[((i, j), k)] >= xs[(i, k)] + xs[(j, k)] - 1,
"lin3 {}{}{}".format(i, j, k),
)
elif i in comps_to_host and j not in comps_to_host:
# Var is free, factor is already hosted
if already_assigned.agent_for(j) == k:
pb += alphas[((i, j), k)] == xs[(i, k)]
else:
pb += alphas[((i, j), k)] == 0
elif i not in comps_to_host and j in comps_to_host:
# if i is not in vars_vars_to_host, it means that it's a
# computation that is already hosted (from hints)
if already_assigned.agent_for(i) == k:
pb += alphas[((i, j), k)] == xs[(j, k)]
else:
pb += alphas[((i, j), k)] == 0
else:
# i and j are both alredy hosted
if (already_assigned.agent_for(i) == k
and already_assigned.agent_for(j) == k):
pb += alphas[((i, j), k)] == 1
else:
pb += alphas[((i, j), k)] == 0
# Now solve our LP
# status = pb.solve(GLPK_CMD())
# status = pb.solve(GLPK_CMD(mip=1))
# status = pb.solve(GLPK_CMD(mip=0, keepFiles=1,
# options=['--simplex', '--interior']))
status = pb.solve(GLPK_CMD(keepFiles=0, msg=False, options=["--pcost"]))
if status != LpStatusOptimal:
raise ImpossibleDistributionException("No possible optimal"
" distribution ")
else:
logger.debug("GLPK cost : %s", pulp.value(pb.objective))
comp_dist = already_assigned
for k in agents_names:
agt_vars = [
i for i, ka in xs if ka == k and pulp.value(xs[(i, ka)]) == 1
]
comp_dist.host_on_agent(k, agt_vars)
return comp_dist
LP += N_electrolyzer_2 * E_electrolyzer_min <= E_2[h]
LP += N_electrolyzer_2 * E_electrolyzer_max >= E_2[h]
LP += N_electrolyzer_3 * E_electrolyzer_min <= E_3[h]
LP += N_electrolyzer_3 * E_electrolyzer_max >= E_3[h]
# Reactor constraints
LP += RNG[h] <= RNG_max
if h != '0':
LP += -RNG_max * tau <= RNG[h] - RNG[str(i - 1)]
LP += RNG_max * tau >= RNG[h] - RNG[str(i - 1)]
# HENG-specific constraints
LP += 0.95 * H2_2[h] <= 0.05 * (NG[h] + RNG[h])
# Integer constraints
LP += pulp.lpSum(n * alpha_1[str(n)] for n in range(1, N_max)) == N_electrolyzer_1
LP += pulp.lpSum(alpha_1) <= 1
LP += pulp.lpSum(n * alpha_2[str(n)] for n in range(1, N_max)) == N_electrolyzer_2
LP += pulp.lpSum(alpha_2) <= 1
LP += pulp.lpSum(n * alpha_3[str(n)] for n in range(1, N_max + 1)) == N_electrolyzer_3
LP += pulp.lpSum(alpha_3) <= 1
# Emission constraints
LP += pulp.lpSum(EMF_comb * RNG[h] + EMF[h] * E_1[h] + EMF_bio * CO2[h] + \
EMF_electrolyzer * H2_1[h] + EMF_reactor * RNG[h] \
for h in [str(x) for x in input_df.index]) == em_rng
LP += pulp.lpSum(EMF_NG * NG[h] + EMF[h] * E_2[h] + EMF_electrolyzer * H2_2[h] \
for h in [str(x) for x in input_df.index]) == em_heng
LP += num_vehicle * FCV_penetration * EMF_vehicle == em_offset_fcv
LP += pulp.lpSum(EMF_NG * NG_demand[h] for h in input_df.index) == em_ng
def _solver(Gt, Gd, target_latency=None):
"""
Args:
target_latency (int): latency constrain for this task graph
source_list (list): source nodes in the task graph
dst_list (list): destination nodes in the task graph
Gt (networkx.DiGraph): task graph in a multi-source, single
destination, no-loop directed graph, where src_k are data sources,
dst is the actuator, and other nodes in between are tasks
src1 -----> t11 -----> t12 ... -----> dst
src2 -----> t21 -----> t22 ... ----->
... ... ... ...
-----> tk1 -----> tk2 ... ----->
Gt.node[t] (dict): node, stores information of each task
Gt[t1][t2] (dict): edge, stores relationship between tasks
E(t1, t2) (Unit): input/output relationship between t1, t2
If t1 will not ouput any data to t2, set the value to 0
e.g. Gt[t1][t2][GtInfo.TRAFFIC] = Unit.byte(20)
Gt[t1][t2][GtInfo.TRAFFIC] = 0
_It(t2) (Unit): total input data size to the task. Obtained
from sum E(ti, t2) for all ti with an edge to t2. The value
will be stored at Gt.node[t][GtInfo.RESRC_RQMT]
_Ot(t1) (Unit): total ouput data size to the task. Obtained
from sum E(t1, ti) for all ti with an edge from t1. The
value will be stored at Gt.node[t][GtInfo.RESRC_RQMT]
Lt(t,d) (Unit): computation latency of task t runs on device d.
Devices can be categorized according to number of CPUs,
GPUs, RAM size, and disk space.
e.g. Gt.node[t][GtInfo.LATENCY_INFO] = {
Device.T2_MICRO: Unit.ms(100),
Device.P3_2XLARGE: Unit.ms(5)}
device_type = Device.type(Gd.node[d][GdInfo.HARDWARE])
Gt.node[t][GtInfo.LATENCY_INFO][device_type] = 100 (ms)
Rt(t) (dict): minimum RESRC requirement for task t
Rt(t,r,d): minimum requirement of RESRC r for task t of a
specific build flavor for that device
e.g. build_type = Flavor.type(Device.P3_2XLARGE)
assert(build_type == Flavor.GPU)
Gt.node[t][GtInfo.RESRC_RQMT][build_type] = {
Hardware.RAM: Unit.gb(2),
Hardware.HD: Unit.mb(512),
Hardware.CPU: Unit.percentage(10),
Hardware.GPU: Unit.percentage(60),
Hardware.CAMERA: 1,
Hardware.NIC_INGRESS: Unit.mb(2), # _It(t)
Hardware.NIC_EGRESS: Unit.byte(20), # _Ot(t)
}
Gd (networkx.DiGraph): a directed graph describes network topology,
where each node represent a device
Gd[d] (dict): information of each device, including:
Ld(d1, d2) (Unit): transmission time between two devices d1, d2
If d2 is not reachable from d1, set the value to MAXINT
e.g. Gd[d1][d2][GdInfo.LATENCY] = 20 (ms)
Gd[d1][d2][GdInfo.LATENCY] = Const.MAXINT
_Hd(d) (dict): hardware specification of device d.
Use this internal information to determine device_type
Dd(t) and calculate Rd(d).
e.g. Gd.node[d][GdInfo.HARDWARE] = {
Hardware.RAM: Unit.gb(16),
Hardware.HD: Unit.tb(1),
Hardware.CPU: 4,
Hardware.GPU: 1,
Hardware.GPS: 1,
Hardware.CAMERA: 1
Hardware.NIC_INGRESS: Unit.gbps(10),
Hardware.NIC_EGRESS: Unit.gbps(10),
}
_Dd(d) (enum): device type of device d, determined by hardware
specification of the device. Used by Gt.node[t] for
accessing information of the a certain device type
e.g. device_type = Device.type(Gd.node[d][GdInfo.HARDWARE])
assert(device_type == Device.T2_MICRO)
Rd(d) (dict): available RESRCs on device d.
Rd(d, r) (Unit): availablity of RESRC r on device d.
e.g. Gd.node[d][GdInfo.RESRC] = {
Hardware.RAM: Unit.gb(12),
Hardware.HD: Unit.gb(500),
Hardware.CPU: Unit.percentage(80),
Hardware.GPU: Unit.percentage(100),
Hardware.BW_INGRESS: Unit.mb(100),
Hardware.BW_EGRESS: Unit.mb(60),
Hardware.GPS: 1,
Hardware.PROXIMITY: 1,
Hardware.ACCELEROMETER: 1,
Hardware.GYROSCOPE: 1,
Hardware.CAMERA: 1,
}
decision variable:
X(t,d) = 1 if assign task t to device d else 0
objective: minimize longest path's overall latency in the task graph,
i.e. total execution times + transmission latencies along the path
len(p)
minimize max { sum ( X(ti,di) * Lt(ti, di) )
X(t,d) p in Gt i=1
len(p)-1
+ sum ( X(ti,di) * X(ti+1,di+1) * Ld(di, di+1) ) }
i=1
this can be simplified by an auxiliary variable and rewrote as:
minimize Y , where Y = max {....} above
with additional constrains:
for p in Gt:
len(p)
Y >= max { sum ( X(ti,di) * Lt(ti, di) )
p in Gt i=1
len(p)-1
+ sum ( X(ti,di) * X(ti+1,di+1) * Ld(di, di+1) ) }
i=1
constrians 1: neighbors in the task graph must also be accessible from
each other in network graph
for (ti, tj) in Gt.edges():
for all combinations (di, dj) in Gd:
X(ti,di) * X(tj,dj) * Ld(di, dj) < LATENCY_MAX
constrians 2: device must be able to support what the tasks need
for d in Gd:
for r in Rd:
len(Gt)
Rd(d,r) - { sum ( X(ti,d) * Rt(ti,r,d) ) } >= 0
i=1
constrains 3: task -> device is one-to-one mapping, and all task must be
mapped to one device
since linear programming cannot have variable multiplication,
use a helper variable XX to replace X(ti,di) * X(tj,dj)
X(ti,di) * X(tj,dj) ---replace---> XX(X(ti,di),X(tj,dj))
with additional constrains
XX(X(ti,di),X(tj,dj)) + 1 >= X(ti,di) + X(tj,dj)
XX(X(ti,di),X(tj,dj)) * 2 <= X(ti,di) + X(tj,dj)
"""
# define invalid_latency as a constrain to filter out meaningless
# solutions
if target_latency:
invalid_latency = target_latency * 2
else:
invalid_latency = Const.INT_MAX
log.info(
('set invalid_latency={latency}. skip neighbors cannot be reached '
'within {latency} ms.').format(latency=invalid_latency))
# Generate all possible mappings of device_i <-> task_i
known_mapping = {}
for node in Gt.nodes():
mapped_device = Gt.node[node].get(GtInfo.DEVICE, None)
if mapped_device is not None:
known_mapping[node] = mapped_device
log.info('known_mapping: {}'.format(known_mapping))
mapped_tasks = list(known_mapping)
mapped_devices = listvalues(known_mapping)
tasks = [t for t in Gt.nodes() if t not in mapped_tasks]
devices = [d for d in Gd.nodes() if d not in mapped_devices]
log.info('find possible mappings for {} tasks in {} devices'.format(
len(tasks), len(devices)))
# create LP problem
prob = pulp.LpProblem("placethings", pulp.LpMinimize)
# auxiliary variable: it represent the longest path's overall latency
# in the task graph
Y = pulp.LpVariable('LongestPathLength',
lowBound=0,
upBound=Const.INT_MAX,
cat='Interger')
# objective: minimize longest path's overall latency in the task graph
# later, add additional constrains for the auxiliary variable
prob += Y
# decision variable: X(t,d) = 1 if assign task t to device d else 0
X = defaultdict(dict)
all_unknown_X = []
for t in tasks:
for d in devices:
X[t][d] = None
for d in mapped_devices:
for t in Gt.nodes():
X[t][d] = 0
for t in mapped_tasks:
for d in Gd.nodes():
X[t][d] = 0
d_known = Gt.node[t][GtInfo.DEVICE]
X[t][d_known] = 1 #Mapping which are added by user
for t in tasks:
for d in devices:
if X[t][d] is None:
X[t][d] = pulp.LpVariable('X_{}_{}'.format(t, d),
lowBound=0,
upBound=1,
cat='Integer')
all_unknown_X.append(X[t][d])
# 1-1 map # One task can only map map to device
prob += pulp.lpSum(listvalues(X[t])) == 1
# number of X == 1 must equal to number of tasks
log.info('there are {} unknowns'.format(len(all_unknown_X)))
assert len(all_unknown_X) == len(tasks) * len(devices)
# tasks are the tasks not yet assigned.
# devices are the devices not yet mapped
prob += pulp.lpSum(all_unknown_X) == len(tasks)
# auxiliary variable: use XX to replace X[ti][di] * X[tj][dj]
XX = defaultdict(dict)
all_XX = []
for ti in Gt.nodes():
for di in Gd.nodes():
for tj in Gt.nodes():
for dj in Gd.nodes():
if (ti, tj) not in Gt.edges():
XX[(ti, di)][(tj, dj)] = 0
elif (di, dj) not in Gd.edges() or (
Gd[di][dj][GdInfo.LATENCY] > invalid_latency):
# constrians 1: neighbors in the task graph must also
# be accessible from each other in the network graph
XX[(ti, di)][(tj, dj)] = 0
else:
XX[(ti, di)][(tj,
dj)] = pulp.LpVariable('XX_{}_{}'.format(
(ti, di), (tj, dj)),
lowBound=0,
upBound=1,
cat='Integer')
all_XX.append(XX[(ti, di)][(tj, dj)])
# add constrains
prob += (XX[(ti, di)][(tj, dj)] + 1 >=
X[ti][di] + X[tj][dj])
prob += (XX[(ti, di)][(tj, dj)] * 2 <=
X[ti][di] + X[tj][dj])
log.info('there are {} combinations for links'.format(len(all_XX)))
prob += pulp.lpSum(all_XX) == len(Gt.edges())
# Generate all simple paths in the graph G from source to target.
src_list, dst_list, all_paths = _find_all_simple_path(Gt)
log.info('find all path from {} to {}'.format(src_list, dst_list))
# Generate all possible mappings
all_mappings = list(pulp.permutation(devices, len(tasks)))
log.info('{} possible mappings for {} devices and {} tasks'.format(
len(all_mappings), len(devices), len(tasks)))
task_to_idx = dict(zip(tasks, range(len(tasks))))
# use constrains to model Y, the longest path for each mapping
for device_mapping in all_mappings:
for path in all_paths:
assert path[0] in src_list
assert path[len(path) - 1] in dst_list
path_vars = []
# first node: src
ti = path[0]
di = Gt.node[ti][GtInfo.DEVICE]
# device_mapping start from path[1] to path[N-1]
for j in range(1, len(path) - 1):
tj = path[j]
dj = device_mapping[task_to_idx[tj]]
assert Gt.node[tj][GtInfo.DEVICE] is None
assert dj in Gd.nodes()
# get transmission latency from di -> dj
Ld_di_dj = Gd[di][dj][GdInfo.LATENCY]
# path_vars.append((X[ti][di] * X[tj][dj]) * Ld_di_dj)
path_vars.append(XX[(ti, di)][(tj, dj)] * Ld_di_dj)
log.debug('Ld_di_dj (move from {} to {}) = {}'.format(
di, dj, Ld_di_dj))
# get computation latency for task tj at dj
dj_type = Gd.node[dj][GdInfo.DEVICE_TYPE]
# get latency of the default build flavor
Lt_tj_dj = listvalues(
Gt.node[tj][GtInfo.LATENCY_INFO][dj_type])[0]
path_vars.append(X[tj][dj] * Lt_tj_dj)
log.debug('Lt_tj_dj (compute {} at {}) = {}'.format(
tj, dj, Lt_tj_dj))
ti = tj
di = dj
# last node: dst
tj = path[len(path) - 1]
dj = Gt.node[tj][GtInfo.DEVICE]
Ld_di_dj = Gd[di][dj][GdInfo.LATENCY]
# path_vars.append(X[ti][di] * X[tj][dj] * Ld_di_dj)
path_vars.append(XX[(ti, di)][(tj, dj)] * Ld_di_dj)
log.debug('Ld_di_dj (move from {} to {}) = {}'.format(
di, dj, Ld_di_dj))
log.debug('add constrain for path:\n Y >= {}'.format(path_vars))
# add constrain
prob += Y >= pulp.lpSum(path_vars)
# constrians 2: device must be able to support what the tasks need
# for d in Gd:
# for r in Rd:
# len(Gt)
# Rd(d,r) - { sum ( X(ti,d) * Rt(ti,r,d) ) } >= 0
# i=1
for di in devices:
for resrc in Hardware:
# get available RESRC or set to 0
Rd_d_r = Gd.node[di][GdInfo.RESRC].get(resrc, 0)
var_list = []
for ti in tasks:
di_type = Gd.node[di][GdInfo.DEVICE_TYPE]
# get the default flavor
ti_flavor = list(Gt.node[ti][GtInfo.LATENCY_INFO][di_type])[0]
Rt_t_r_d = (Gt.node[ti][GtInfo.RESRC_RQMT][ti_flavor].get(
resrc, 0))
if Rt_t_r_d > 0:
var_list.append(X[ti][di] * Rt_t_r_d)
if var_list:
log.debug('add constrain for {}({}):\n {} <= {}'.format(
di, resrc, var_list, Rd_d_r))
prob += pulp.lpSum(var_list) <= Rd_d_r
# solve
status = prob.solve(pulp.solvers.GLPK(msg=1))
log.info('status={}'.format(pulp.LpStatus[status]))
result_mapping = {}
for t in Gt.nodes():
for d in Gd.nodes():
if pulp.value(X[t][d]):
log.info('map: {} <-> {}, X_t_d={}'.format(
t, d, pulp.value(X[t][d])))
result_mapping[t] = d
return status, result_mapping
def outer_recur_solve(self, temp_leximin, leximin_counts,
return_assignment=False):
next_c_star = pulp.LpVariable(
'next_leximin', 0, temp_leximin
)
x = pulp.LpVariable.dicts(
'assignment',
[(agent_id, intv_id)
for agent_id in range(self.n_agents)
for intv_id in range(self.n_intvs)],
cat='Binary'
)
prob = pulp.LpProblem()
prob += next_c_star
# Assignment constraint
for agent_id in range(self.n_agents):
prob += pulp.lpSum(
x[(agent_id, intv_id)]
for intv_id in range(self.n_intvs)
) == 1
# Capacity constraints
for intv_id in range(self.n_intvs):
prob += pulp.lpSum(
x[(agent_id, intv_id)]
for agent_id in range(self.n_agents)
) <= self.capacities[intv_id]
# Leximin count constraints
for leximin in leximin_counts:
prob += pulp.lpSum(
x[(agent_id, intv_id)]
for agent_id, intv_id in np.argwhere(self.cost_matrix == leximin)
) == leximin_counts[leximin]
# Constraints for next leximin
for agent_id in range(self.n_agents):
for intv_id in range(self.n_intvs):
temp_cost = self.cost_matrix[agent_id, intv_id]
if temp_cost not in leximin_counts:
prob += x[(agent_id, intv_id)] * temp_cost \
<= next_c_star
status = prob.solve(solver=pulp.solvers.GUROBI_CMD())
if pulp.LpStatus[status] == 'Optimal':
if return_assignment:
assignments = np.zeros((self.n_agents,), dtype=int)
for agent_id in range(self.n_agents):
for intv_id in range(self.n_intvs):
if x[(agent_id, intv_id)].varValue == 1:
assignments[agent_id] = intv_id
return next_c_star.varValue, assignments
return next_c_star.varValue
return False
def solve(professors, courses, semesters, slots):
prob = pulp.LpProblem("Semester Problem", pulp.LpMaximize)
opt_terms = []
for k, v in lp_vars.items():
if k[2] in n1s + n2s:
opt_terms.append(100 * lp_vars_rev[v.name][2].size * v)
else:
opt_terms.append(lp_vars_rev[v.name][2].size * v)
opt_fun = pulp.lpSum(lp_vars_rev[v.name][2].size * v
for k, v in lp_vars.items())
prob += opt_fun
#print(lp_vars.keys())
# Maximum one class for slot
for s in slots:
for sem in semesters:
v = pulp.lpSum(lp_vars[(p, c, s, sem)] \
for p in professors \
for c in courses)
prob += v <= 1 #s.size
#for sem in semesters:
# for s in sem.slots:
# v = pulp.lpSum(lp_vars[(p, c, s, sem)] \
# for p in professors \
# for c in courses)
# prob += v <= 1 #s.size
for s in slots:
for p in professors:
v = pulp.lpSum(lp_vars[(p, c, s, sem)] \
for sem in semesters \
for c in courses)
prob += v <= 1 #s.size
# previne que haja choque entre os professores das turmas conjuntas
for s in slots:
a = pulp.lpSum(lp_vars[(priscila, c, s, sem)] \
for sem in semesters \
for c in courses)
b = pulp.lpSum(lp_vars[(guilherme, c, s, sem)] \
for sem in semesters \
for c in courses)
c = pulp.lpSum(lp_vars[(priscila_guilherme, c, s, sem)] \
for sem in semesters \
for c in courses)
prob += a + c <= 1 #s.size
prob += b + c <= 1 #s.size
# previne que haja choque entre os professores das turmas conjuntas
for s in slots:
a = pulp.lpSum(lp_vars[(padilha, c, s, sem)] \
for sem in semesters \
for c in courses)
b = pulp.lpSum(lp_vars[(emilio, c, s, sem)] \
for sem in semesters \
for c in courses)
c = pulp.lpSum(lp_vars[(emilio_padilha, c, s, sem)] \
for sem in semesters \
for c in courses)
prob += a + c <= 1 #s.size
prob += b + c <= 1 #s.size
for s in sex_noite:
for sem in semesters:
v = pulp.lpSum(lp_vars[(priscila_guilherme, c, s, sem)] \
for c in courses)
prob += v == 0 #s.size
for s in sex_noite:
for sem in semesters:
v = pulp.lpSum(lp_vars[(guilherme, c, s, sem)] \
for c in courses)
prob += v == 0 #s.size
for s in sex_noite:
for sem in semesters:
v = pulp.lpSum(lp_vars[(raquel, c, s, sem)] \
for c in courses)
prob += v == 0 #s.size
# Each professor must only give his classes
for p in professors:
v = pulp.lpSum(lp_vars[(p, c, s, sem)] * s.size \
for s in slots \
for c in p.courses \
for sem in semesters )
prob += v <= sum(c.num_hours for c in p.courses)
# Each professor must not give other professor's classes
for p in professors:
v = pulp.lpSum(lp_vars[(p, c, s, sem)] \
for s in slots \
for c in courses if not c in p.courses \
for sem in semesters )
prob += v == 0
# Each course must be complete
for c in cs:
v = pulp.lpSum(lp_vars[(p, c, s, sem)] * s.size \
for s in slots \
for p in ps \
for sem in semesters )
prob += v <= c.num_hours
# Each semester must have all its courses filled
#for sem in semesters:
# v = pulp.lpSum(lp_vars[(p, c, s, sem)] * s.size \
# for s in sem.slots \
# for p in ps \
# for c in sem.courses)
# prob += v <= sum(c.num_hours for c in sem.courses)
# Each semester must only have its courses # tested
for sem in semesters:
v = pulp.lpSum(lp_vars[(p, c, s, sem)] \
for s in slots \
for p in ps \
for c in cs if not c in sem.courses)
prob += v == 0
# Each semester must only have its slots # tested
for sem in semesters:
v = pulp.lpSum(lp_vars[(p, c, s, sem)] \
for s in slots if not s in sem.slots \
for p in ps \
for c in cs)
prob += v == 0
# aplica as restrições legais
for p in professors:
#print(p, proibidos)
for (a, b) in proibidos:
va = pulp.lpSum(lp_vars[(p, c, a, sem)] \
for c in p.courses \
for sem in semesters )
vb = pulp.lpSum(lp_vars[(p, c, b, sem)] \
for c in p.courses \
for sem in semesters )
prob += va + vb <= 1
# aplica restrições legais a turmas cojuntas
for (a, b) in proibidos:
va1 = pulp.lpSum(lp_vars[(priscila, c, b, sem)] \
for c in courses \
for sem in semesters )
va2 = pulp.lpSum(lp_vars[(guilherme, c, b, sem)] \
for c in courses \
for sem in semesters )
vb = pulp.lpSum(lp_vars[(priscila_guilherme, c, a, sem)] \
for c in courses \
for sem in semesters )
prob += va1 + vb <= 1
prob += va2 + vb <= 1
va1 = pulp.lpSum(lp_vars[(emilio, c, b, sem)] \
for c in courses \
for sem in semesters )
va2 = pulp.lpSum(lp_vars[(padilha, c, b, sem)] \
for c in courses \
for sem in semesters )
vb = pulp.lpSum(lp_vars[(emilio_padilha, c, a, sem)] \
for c in courses \
for sem in semesters )
prob += va1 + vb <= 1
prob += va2 + vb <= 1
va1 = pulp.lpSum(lp_vars[(priscila, c, a, sem)] \
for c in courses \
for sem in semesters )
va2 = pulp.lpSum(lp_vars[(guilherme, c, a, sem)] \
for c in courses \
for sem in semesters )
vb = pulp.lpSum(lp_vars[(priscila_guilherme, c, b, sem)] \
for c in courses \
for sem in semesters )
prob += va1 + vb <= 1
prob += va2 + vb <= 1
va1 = pulp.lpSum(lp_vars[(emilio, c, a, sem)] \
for c in courses \
for sem in semesters )
va2 = pulp.lpSum(lp_vars[(padilha, c, a, sem)] \
for c in courses \
for sem in semesters )
vb = pulp.lpSum(lp_vars[(emilio_padilha, c, b, sem)] \
for c in courses \
for sem in semesters )
prob += va1 + vb <= 1
prob += va2 + vb <= 1
# previne aulas faixa
for p in professors:
if not p.faixa:
for (a, b) in evitar:
va = pulp.lpSum(lp_vars[(p, c, a, sem)] \
for c in p.courses \
for sem in semesters )
vb = pulp.lpSum(lp_vars[(p, c, b, sem)] \
for c in p.courses \
for sem in semesters )
prob += va + vb <= 1
prob.solve()
print("Status:", pulp.LpStatus[prob.status])
#for v in prob.variables():
# if v.varValue and v.varValue > 0:
# print(v.name, "=", v.varValue)
def get_slot(s_, sem_):
for (p, c, s, sem), v in lp_vars.items():
if sem_ is sem and s is s_ and pulp.value(v) > 0: #.varValue > 0:
return lp_vars_rev[v.name]
#print("\n\n\n")
def print_m_(ms, sem):
linha = []
for s in ms:
x = get_slot(s, sem)
if x:
label = "%s %s" % (x[0].name, x[1].name)
else:
label = "()"
linha.append(label.center(16))
print(",".join(linha))
for sem in semesters:
print("\n\n", sem)
print("7:30, ", end=' ')
print_m_(m1s, sem)
print("10:10,", end=' ')
print_m_(m2s, sem)
print("13:30,", end=' ')
print_m_(t1s, sem)
print("16:10,", end=' ')
print_m_(t2s, sem)
print("19:10,", end=' ')
print_m_(n1s, sem)
print("21:00,", end=' ')
print_m_(n2s, sem)
print("Total Value:", pulp.value(prob.objective))
print("\nCCR c/ carga horário insuficiente:")
for c in cs:
v = pulp.lpSum(lp_vars[(p, c, s, sem)] \
for s in slots \
for p in ps \
for sem in semesters)
if pulp.value(v) < 2:
print("CCR:", c.name, "HS:", pulp.value(v))
lowBound=0,
cat="Integer")
else:
continue
var.update(var)
# Model
model = LpProblem("Maximum_flow_problem", LpMaximize)
# Goal function
goal_function: List = []
for x in var.keys():
if x[0] == source:
goal_function.append(var[x])
model += lpSum(goal_function)
# Constrains
constrains_o: List = []
constrains_d: List = []
for node in nodes:
for x in var.keys():
if node == x[0]:
constrains_o.append(var[x])
elif node == x[1]:
constrains_d.append(var[x])
else:
continue
def recur_solve_debug(self, temp_leximin, leximin_counts, assigned_agents,
updated_capacities, return_assignment=False):
next_c_star = pulp.LpVariable(
'next_leximin', 0, temp_leximin#, cat='Integer'
)
x = pulp.LpVariable.dicts(
'assignment',
[(agent_id, intv_id)
for agent_id in range(self.n_agents)
for intv_id in range(self.n_intvs)
if agent_id not in assigned_agents],
cat='Binary'
)
prob = pulp.LpProblem()
prob += next_c_star
# Assignment constraint
for agent_id in range(self.n_agents):
if agent_id not in assigned_agents:
prob += pulp.lpSum(
x[(agent_id, intv_id)]
for intv_id in range(self.n_intvs)
) == 1
# Capacity constraints
for intv_id in range(self.n_intvs):
prob += pulp.lpSum(
x[(agent_id, intv_id)]
for agent_id in range(self.n_agents)
if agent_id not in assigned_agents
) <= updated_capacities[intv_id]
# Leximin count constraints
# fixed allocations do not contain non-unique lexima in this dict.
for leximin in leximin_counts:
prob += pulp.lpSum(
x[(agent_id, intv_id)]
for agent_id in range(self.n_agents)
for intv_id in range(self.n_intvs)
if agent_id not in assigned_agents and self.cost_matrix[agent_id, intv_id] == leximin
) == leximin_counts[leximin]
# Constraints for next leximin
for agent_id in range(self.n_agents):
if agent_id not in assigned_agents:
for intv_id in range(self.n_intvs):
temp_cost = self.cost_matrix[agent_id, intv_id]
if temp_cost not in leximin_counts:
prob += x[(agent_id, intv_id)] * temp_cost \
<= next_c_star
status = prob.solve(solver=pulp.solvers.GUROBI_CMD())
if pulp.LpStatus[status] == 'Optimal':
if return_assignment:
assignments = np.zeros((self.n_agents,), dtype=int)
for agent_id in range(self.n_agents):
if agent_id not in assigned_agents:
for intv_id in range(self.n_intvs):
if x[(agent_id, intv_id)].varValue == 1:
assignments[agent_id] = intv_id
return next_c_star.varValue, assignments
return next_c_star.varValue
return False
