def __init__(self, model: mip.Model):
if model.status == mip.OptimizationStatus.LOADED:
logger.debug("model not runned yet, checking if feasible or not")
model.emphasis = 1 # feasibility
model.preprocess = 1 # -1 automatic, 0 off, 1 on.
model.optimize()
assert (model.status == mip.OptimizationStatus.INFEASIBLE
), "model is not linear infeasible"
self.model = model
def __init__(self, model: mip.Model):
if model.status == mip.OptimizationStatus.LOADED:
print("model not runned yet, checking if feasible or not")
model.emphasis = 1 # feasibility
model.preprocess = 1 # -1 automatic, 0 off, 1 on.
model.optimize()
assert (
model.status == mip.OptimizationStatus.INFEASIBLE
), "model is not linear infeasible"
self.model = model
self.iis_num_iterations = 0
self.iis_iterations = []
self.relax_slack_iterations = []
def build_infeasible_cont_model(num_constraints: int = 10,
num_infeasible_sets: int = 20) -> Model:
# build an infeasible model, based on many redundant constraints
mdl = Model(name='infeasible_model_continuous')
var = mdl.add_var(name='var', var_type=CONTINUOUS, lb=-1000, ub=1000)
for idx, rand_constraint in enumerate(np.linspace(1, 1000,
num_constraints)):
crt = mdl.add_constr(var >= rand_constraint,
name='lower_bound_{}'.format(idx))
logger.debug('added {} to the model'.format(crt))
num_constraint_inf = int(num_infeasible_sets / num_constraints)
for idx, rand_constraint in enumerate(
np.linspace(-1000, -1, num_constraint_inf)):
crt = mdl.add_constr(var <= rand_constraint,
name='upper_bound_{}'.format(idx))
logger.debug('added {} to the model'.format(crt))
mdl.emphasis = 1 # feasibility
mdl.preprocess = 1 # -1 automatic, 0 off, 1 on.
# mdl.pump_passes TODO configure to feasibility emphasis
return mdl
def tsp_mip_solver(input_data):
# parse the input
lines = input_data.split('\n')
nodeCount = int(lines[0])
points = []
for i in range(1, nodeCount + 1):
line = lines[i]
parts = line.split()
points.append(Point(float(parts[0]), float(parts[1])))
print('Points parsed!')
# calculate distance matrix
d_m = [[length(q, p) for q in points] for p in points]
print('Distance matrix ready!')
# declare MIP model
m = Model(solver_name='GRB')
print('-Model instatiated!', datetime.datetime.now())
# states search emphasis
# - '0' (default) balanced approach
# - '1' (feasibility) aggressively searches for feasible solutions
# - '2' (optimality) explores search space to tighten dual gap
m.emphasis = 0
# whenever the distance of the lower and upper bounds is less or
# equal max_gap*100%, the search can be finished
m.max_gap = 0.05
# specifies number of used threads
# 0 uses solver default configuration,
# -1 uses the number of available processing cores
# ≥1 uses the specified number of threads.
# An increased number of threads may improve the solution time but also increases
# the memory consumption. Each thread needs to store a different model instance!
m.threads = 0
# controls the generation of cutting planes
# cutting planes usually improve the LP relaxation bound but also make the solution time of the LP relaxation larger
# -1 means automatic
# 0 disables completely
# 1 (default) generates cutting planes in a moderate way
# 2 generates cutting planes aggressively
# 3 generates even more cutting planes
m.cuts = -1
m.preprocess = 1
m.pump_passes = 20
m.sol_pool_size = 1
nodes = set(range(nodeCount))
# instantiate "entering and leaving" variables
x = [[m.add_var(name="x{}_{}".format(p, q), var_type='B') for q in nodes]
for p in nodes]
# instantiate subtour elimination variables
y = [m.add_var(name="y{}".format(i)) for i in nodes]
print('-Variables instantiated', datetime.datetime.now())
# declare objective function
m.objective = minimize(
xsum(d_m[i][j] * x[i][j] for i in nodes for j in nodes))
print('-Objective declared!', datetime.datetime.now())
# declare constraints
# leave each city only once
for i in tqdm(nodes):
m.add_constr(xsum(x[i][j] for j in nodes - {i}) == 1)
# enter each city only once
for i in tqdm(nodes):
m.add_constr(xsum(x[j][i] for j in nodes - {i}) == 1)
# subtour elimination constraints
for (i, j) in tqdm(product(nodes - {0}, nodes - {0})):
if i != j:
m.add_constr(y[i] - (nodeCount + 1) * x[i][j] >= y[j] - nodeCount)
print('-Constraints declared!', datetime.datetime.now())
#Maximum time in seconds that the search can go on if a feasible solution
#is available and it is not being improved
mssi = 1000 #default = inf
# specifies maximum number of nodes to be explored in the search tree (default = inf)
mn = 1000000 #default = 1073741824
# optimize model m within a processing time limit of 'ms' seconds
ms = 3000 #default = inf
# executes the optimization
print('-Optimizer start.', datetime.datetime.now())
#status = m.optimize(max_seconds = ms,max_seconds_same_incumbent = mssi,max_nodes = mn)
status = m.optimize(max_seconds=ms, max_seconds_same_incumbent=mssi)
print('Opt. Status:', status)
print('MIP Sol. Obj.:', m.objective_value)
print('Dual Bound:', m.objective_bound)
print('Dual gap:', m.gap)
sol = [0]
c_node = 0
for j in range(nodeCount - 1):
for i in range(nodeCount):
if round(m.var_by_name("x{}_{}".format(c_node, i)).x) != 0:
sol.append(i)
c_node = i
break
obj = m.objective_value
# prepare the solution in the specified output format
if status == OptimizationStatus.OPTIMAL:
output_data = '%.2f' % obj + ' ' + str(1) + '\n'
output_data += ' '.join(map(str, sol))
elif status == OptimizationStatus.FEASIBLE:
output_data = '%.2f' % obj + ' ' + str(0) + '\n'
output_data += ' '.join(map(str, sol))
return output_data
def fl_mip_solver(input_data):
# Modify this code to run your optimization algorithm
# parse the input
lines = input_data.split('\n')
parts = lines[0].split()
facility_count = int(parts[0])
customer_count = int(parts[1])
facilities = []
for i in range(1, facility_count+1):
parts = lines[i].split()
facilities.append(Facility(i-1, float(parts[0]), int(parts[1]), Point(float(parts[2]), float(parts[3])) ))
customers = []
for i in range(facility_count+1, facility_count+1+customer_count):
parts = lines[i].split()
customers.append(Customer(i-1-facility_count, int(parts[0]), Point(float(parts[1]), float(parts[2]))))
print('F count:',facility_count)
print('C count:',customer_count)
# instantiates setup cost vector
set_cost = [f.setup_cost for f in facilities]
# instantiates proportional capacity matrix such that Cap[i,j] represents the demand of
# customer 'j' as a fraction of the total capacity of facility 'i'
Cap = [[c.demand/f.capacity for c in customers] for f in facilities]
# instantiates distance matrix in such a way that "sum_{j \in M} D[i,j]*at[i,j]" is the total
# distance cost for facility 'i'
D = [[length(c.location, f.location) for c in customers] for f in facilities]
# declares MIP model
#m = Model(solver_name=CBC)
m = Model(solver_name='GRB')
print('-Model instatiated!',datetime.datetime.now())
# states search emphasis
# - '0' (default) balanced approach
# - '1' (feasibility) aggressively searches for feasible solutions
# - '2' (optimality) explores search space to tighten dual gap
m.emphasis = 2
# whenever the distance of the lower and upper bounds is less or
# equal max_gap*100%, the search can be finished
m.max_gap = 0.05
# specifies number of used threads
# 0 uses solver default configuration,
# -1 uses the number of available processing cores
# ≥1 uses the specified number of threads.
# An increased number of threads may improve the solution time but also increases
# the memory consumption. Each thread needs to store a different model instance!
m.threads = -1
# controls the generation of cutting planes
# cutting planes usually improve the LP relaxation bound but also make the solution time of the LP relaxation larger
# -1 means automatic
# 0 disables completely
# 1 (default) generates cutting planes in a moderate way
# 2 generates cutting planes aggressively
# 3 generates even more cutting planes
m.cuts=-1
m.preprocess=1
m.pump_passes=10
m.sol_pool_size=1
# instantiates open facilities variables
op = [m.add_var(name="op{}".format(i),var_type='B') for i in range(facility_count)]
# instantiates matrix of atribution variables
# (line 'i' is an atribution vector for facility 'i')
# e.g.: At[3][4] returns whether customer 4 is assigned to facility 3
At = [[m.add_var(name="At{},{}".format(i,j) ,var_type='B') for j in range(customer_count)]
for i in range(facility_count)]
print('-Variables declared!',datetime.datetime.now())
# instantiates objective function
# m.objective = minimize("setup costs" + "distance costs")
# Form example:
# m.objective = minimize(
# xsum(dist[i, j] * x[i, j] for (i, j) in product(F, C)) + xsum(y[i] for i in F) )
m.objective = minimize(
xsum(set_cost[i]*op[i] for i in range(facility_count)) +
xsum(sum(D[i][j]*At[i][j] for j in range(customer_count)) for i in range(facility_count))
)
print('-Objective declared!',datetime.datetime.now())
# instatiates capacity constraints
# -can be expressed as "sum_{j \in M} Cap[i,j]*At[i,j] <= op[i]" for all facilities 'i'
# -if a facility is closed (op[i]=0), its effective capacity is 0
for i in range(facility_count):
m.add_lazy_constr( xsum(Cap[i][j]*At[i][j] for j in range(customer_count)) <= op[i] )
# instantiates assignment constraints (maximum of 1 facility per customer)
# -can be expressed as: "sum of elements over of each column of 'At' == 1"
for i in range(customer_count):
m += xsum(At[j][i] for j in range(facility_count)) == 1
print('-Contraints processed!',datetime.datetime.now())
#Maximum time in seconds that the search can go on if a feasible solution
#is available and it is not being improved
mssi = 1000 #default = inf
# specifies maximum number of nodes to be explored in the search tree (default = inf)
mn = 40000 #default = 1073741824
# optimize model m within a processing time limit of 'ms' seconds
ms = 3000 #default = inf
print('-Optimizer start.',datetime.datetime.now())
# executes the optimization
#status = m.optimize(max_seconds = ms,max_seconds_same_incumbent = mssi,max_nodes = mn)
status = m.optimize(max_seconds = ms , max_seconds_same_incumbent = mssi)
final_obj = m.objective_value
print('Opt. Status:',status)
print('MIP Sol. Obj.:',final_obj)
print('Dual Bound:',m.objective_bound)
print('Dual gap:',m.gap)
used=[i for i in range(facility_count) if m.vars[i].x==1]
solution=[None] * customer_count
for i in range(facility_count):
for j in range(customer_count):
if round(m.vars[i*customer_count + j + facility_count].x) == 1:
solution[j]=i
# parse output varibles and convert to conventional solution output format
# calculate the cost of the solution
obj = sum([facilities[f].setup_cost for f in used])
for customer in customers:
obj += length(customer.location, facilities[solution[customer.index]].location)
if status == OptimizationStatus.OPTIMAL:
# prepare the solution in the specified output format
output_data = '%.2f' % obj + ' ' + str(1) + '\n'
output_data += ' '.join(map(str, solution))
elif status == OptimizationStatus.FEASIBLE:
output_data = '%.2f' % obj + ' ' + str(0) + '\n'
output_data += ' '.join(map(str, solution))
return output_data
def mip_solver(input_data):
# Modify this code to run your optimization algorithm
# parse the input
lines = input_data.split('\n')
firstLine = lines[0].split()
item_count = int(firstLine[0])
capacity = int(firstLine[1])
items = []
for i in range(1, item_count+1):
line = lines[i]
parts = line.split()
items.append(Item(i-1, int(parts[0]), int(parts[1])))
# a trivial algorithm for filling the knapsack
# it takes items in-order until the knapsack is full
value = 0
weight = 0
taken = [0]*len(items)
# declare value vector
val_vec = [item.value for item in items]
# declare weight vector scaled by capacity
wgt_vec = [item.weight/capacity for item in items]
# start MIP solver
m = Model(solver_name='GRB')
print('-Model instatiated!',datetime.datetime.now())
# states search emphasis
# - '0' (default) balanced approach
# - '1' (feasibility) aggressively searches for feasible solutions
# - '2' (optimality) explores search space to tighten dual gap
m.emphasis = 2
# whenever the distance of the lower and upper bounds is less or
# equal max_gap*100%, the search can be finished
m.max_gap = 0.05
# specifies number of used threads
# 0 uses solver default configuration,
# -1 uses the number of available processing cores
# ≥1 uses the specified number of threads.
# An increased number of threads may improve the solution time but also increases
# the memory consumption. Each thread needs to store a different model instance!
m.threads = -1
# controls the generation of cutting planes
# cutting planes usually improve the LP relaxation bound but also make the solution time of the LP relaxation larger
# -1 means automatic
# 0 disables completely
# 1 (default) generates cutting planes in a moderate way
# 2 generates cutting planes aggressively
# 3 generates even more cutting planes
m.cuts=-1
m.preprocess=1
m.pump_passes=10
m.sol_pool_size=1
# instantiates taken items variable vector
taken = [m.add_var(name="it{}".format(i),var_type='B') for i in range(item_count)]
print('-Variables declared!',datetime.datetime.now())
# instantiates objective function
m.objective = maximize( xsum( val_vec[i]*taken[i] for i in range(item_count) ) )
print('-Objective declared!',datetime.datetime.now())
m.add_constr( xsum( wgt_vec[i]*taken[i] for i in range(item_count)) <=1 )
print('-Contraints processed!',datetime.datetime.now())
#Maximum time in seconds that the search can go on if a feasible solution
#is available and it is not being improved
mssi = 1000 #default = inf
# specifies maximum number of nodes to be explored in the search tree (default = inf)
mn = 40000 #default = 1073741824
# optimize model m within a processing time limit of 'ms' seconds
ms = 3000 #default = inf
print('-Optimizer start.',datetime.datetime.now())
# executes the optimization
#status = m.optimize(max_seconds = ms,max_seconds_same_incumbent = mssi,max_nodes = mn)
status = m.optimize(max_seconds = ms , max_seconds_same_incumbent = mssi)
final_obj = m.objective_value
print('Opt. Status:',status)
print('MIP Sol. Obj.:',final_obj)
print('Dual Bound:',m.objective_bound)
print('Dual gap:',m.gap)
sol = [round(it.x) for it in taken]
value = int(final_obj)
taken = sol
# prepare the solution in the specified output format
if status == OptimizationStatus.OPTIMAL:
output_data = str(value) + ' ' + str(1) + '\n'
output_data += ' '.join(map(str, taken))
elif status == OptimizationStatus.FEASIBLE:
output_data = str(value) + ' ' + str(0) + '\n'
output_data += ' '.join(map(str, taken))
if item_count == 10000:
output_data=greedy(input_data)
return output_data
# denominator of the expected value
m += xsum(w[i]*(d - (y[1][i] - z[1][i])) for i in range(M)) == 1, "denominator"
# if not antidisabling, turn each antidisable off
if not ANTIDISABLE:
for i in range(len(z[0])):
m += z[0][i] == 0, f"{zi.name}0"
m += z[1][i] == 0, f"{zi.name}1"
### objective: maximize expected value of the remaining characters
# numerator of the expected value, denominator has been accounted for
m.objective = xsum(t[i]*(d - (y[1][i] - z[1][i])) for i in range(M))
if __name__ == "__main__":
m.emphasis = 2 # emphasize optimality
m.preprocess = 1 # don't preprocess if it introduces error
status = m.optimize()
disable_list = [bundle_list[i] for i in range(N) if x[i].x >= 0.99] + \
[series_list[i - N] for i in range(N, N + A) if x[i].x >= 0.99]
antidisable_list = [series_list[i] for i in range(M) if z[0][i].x >= 0.99]
total = get_size(disable_list)
count, count_anti = get_wa(disable_list), get_wa(antidisable_list)
X, p = random_variable(character_values(disable_list, antidisable_list))
print(f"expected value: {E(p, X):.3f}")
print(f"disablelist ({len(disable_list)}/{NUM_DISABLE})")
print(f"{server_disabled + total} disabled ({server_wa + count} $wa)")
print(f"Overlap limit: {total} / {OVERLAP} characters")
print(f"{count} $wa characters disabled by $disable")
