def solve(self, *args, **kw):
if len(args) > 1:
self.err('''
incorrect number of arguments for solve(),
must be at least 1 (solver), other must be keyword arguments''')
solver = args[0] if len(args) != 0 else kw.get('solver', self.solver)
graph = self.graph # must be networkx instance
nodes = graph.nodes()
edges = graph.edges()
n = len(nodes)
node2index = dict([(node, i) for i, node in enumerate(nodes)])
index2node = dict([(i, node) for i, node in enumerate(nodes)])
import FuncDesigner as fd, openopt
x = fd.oovars(n, domain=bool)
objective = fd.sum(x)
startPoint = {x: [0] * n}
fixedVars = {}
includedNodes = getattr(kw, 'includedNodes', None)
if includedNodes is None:
includedNodes = getattr(self, 'includedNodes', ())
for node in includedNodes:
fixedVars[x[node2index[node]]] = 1
excludedNodes = getattr(kw, 'excludedNodes', None)
if excludedNodes is None:
excludedNodes = getattr(self, 'excludedNodes', ())
for node in excludedNodes:
fixedVars[x[node2index[node]]] = 0
if openopt.oosolver(solver).__name__ == 'interalg':
constraints = [
fd.NAND(x[node2index[i]], x[node2index[j]]) for i, j in edges
]
P = openopt.GLP
else:
constraints = [
x[node2index[i]] + x[node2index[j]] <= 1 for i, j in edges
]
P = openopt.MILP
p = P(objective,
startPoint,
constraints=constraints,
fixedVars=fixedVars,
goal='max')
for key, val in kw.items():
setattr(p, key, val)
r = p.solve(solver, **kw)
r.solution = [index2node[i] for i in range(n) if r.xf[x[i]] == 1]
r.ff = len(r.solution)
return r
def startOptimization(self, root, varsRoot, AddVar, currValues, \
ValsColumnName, ObjEntry, ExperimentNumber, Next, NN, goal, objtol, C):
AddVar.destroy()
ValsColumnName.set('Experiment parameters')
n = len(self.NameEntriesList)
Names, Lb, Ub, Tol, x0 = [], [], [], [], []
for i in range(n):
N, L, U, T, valEntry = \
self.NameEntriesList[i], self.LB_EntriesList[i], self.UB_EntriesList[i], self.TolEntriesList[i], self.ValueEntriesList[i]
N.config(state=DISABLED)
L.config(state=DISABLED)
U.config(state=DISABLED)
T.config(state=DISABLED)
#valEntry.config(state=DISABLED)
name, lb, ub, tol, val = N.get(), L.get(), U.get(), T.get(), valEntry.get()
Names.append(name)
x0.append(float(val))
Lb.append(float(lb) if lb != '' else -inf)
Ub.append(float(ub) if ub != '' else inf)
# TODO: fix zero
Tol.append(float(tol) if tol != '' else 0)
x0, Tol, Lb, Ub = asfarray(x0), asfarray(Tol), asfarray(Lb), asfarray(Ub)
x0 *= xtolScaleFactor / Tol
#self.x0 = copy(x0)
from openopt import NLP, oosolver
p = NLP(objective, x0, lb = Lb * xtolScaleFactor / Tol, ub=Ub * xtolScaleFactor / Tol)
self.prob = p
#calculated_points = [(copy(x0), copy(float(ObjEntry.get())))
p.args = (Tol, self, ObjEntry, p, root, ExperimentNumber, Next, NN, objtol, C)
#p.graphics.rate = -inf
#p.f_iter = 2
solver = oosolver('bobyqa', useStopByException = False)
p.solve(solver, iprint = 1, goal = goal)#, plot=1, xlabel='nf')
self.solved = True
if p.stopcase >= 0:
self.ValsColumnName.set('Best parameters')
NN.set('Best obtained objective value:')
#Next.config(state=DISABLED)
Next.destroy()
#reverse = True if goal == 'min' else False
calculated_items = self.calculated_points.items() if isinstance(self.calculated_points, dict) else self.calculated_points
vals = [calculated_items[i][1] for i in range(len(calculated_items))]
ind = argsort(vals)
j = ind[0] if goal == 'min' else ind[-1]
key, val = calculated_items[j]
text_coords = key.split(' ')
for i in range(len(self.ValueEntriesList)):
self.ValueEntriesList[i].delete(0, END)
self.ValueEntriesList[i].insert(0, text_coords[i])
ObjEntry.delete(0, END)
obj_tol = self.ObjTolEntry.get()
val = float(val) * 1e4 * objtol
ObjEntry.insert(0, str(val))
ObjEntry.config(state=DISABLED)
def solve(self, *args, **kw):
if len(args) > 1:
self.err('''
incorrect number of arguments for solve(),
must be at least 1 (solver), other must be keyword arguments''')
solver = args[0] if len(args) != 0 else kw.get('solver', self.solver)
graph = self.graph # must be networkx instance
nodes = graph.nodes()
edges = graph.edges()
n = len(nodes)
node2index = dict([(node, i) for i, node in enumerate(nodes)])
index2node = dict([(i, node) for i, node in enumerate(nodes)])
import FuncDesigner as fd, openopt
x = fd.oovars(n, domain=bool)
objective = fd.sum(x)
startPoint = {x:[0]*n}
fixedVars = {}
includedNodes = getattr(kw, 'includedNodes', None)
if includedNodes is None:
includedNodes = getattr(self, 'includedNodes', ())
for node in includedNodes:
fixedVars[x[node2index[node]]] = 1
excludedNodes = getattr(kw, 'excludedNodes', None)
if excludedNodes is None:
excludedNodes = getattr(self, 'excludedNodes', ())
for node in excludedNodes:
fixedVars[x[node2index[node]]] = 0
if openopt.oosolver(solver).__name__ == 'interalg':
constraints = [fd.NAND(x[node2index[i]], x[node2index[j]]) for i, j in edges]
P = openopt.GLP
else:
constraints = [x[node2index[i]]+x[node2index[j]] <=1 for i, j in edges]
P = openopt.MILP
p = P(objective, startPoint, constraints = constraints, fixedVars = fixedVars, goal = 'max')
for key, val in kw.items():
setattr(p, key, val)
r = p.solve(solver, **kw)
r.solution = [index2node[i] for i in range(n) if r.xf[x[i]] == 1]
r.ff = len(r.solution)
return r
def _solve(self, *args, **kwargs):
#!!!!! TODO: determing LP, MILP, MINLP if possible
try:
import openopt
except ImportError:
raise FuncDesignerException(
'to perform the operation you should have openopt installed')
constraints = self._getAllConstraints()
# TODO: mention it in doc
if 'constraints' in kwargs:
tmp = set(kwargs['constraints'])
tmp.update(set(constraints))
kwargs['constraints'] = tmp
else:
kwargs['constraints'] = constraints
freeVars, fixedVars = kwargs.get('freeVars',
None), kwargs.get('fixedVars', None)
isSystemOfEquations = kwargs['goal'] == 'solution'
isLinear = all([(c.oofun.getOrder(freeVars, fixedVars) < 2)
for c in constraints])
if isSystemOfEquations:
if isLinear: # Linear equations system
p = sle(list(kwargs['constraints']), *args, **kwargs)
else: # Nonlinear equations system
f = kwargs['constraints']
############################################
#cons = self._getAllConstraints()
# OLD
# if not all([array_equal(c.lb, c.ub) for c in f]):
# raise FuncDesignerException('Solving constrained nonlinear systems is not implemented for oosystem yet, use SNLE constructor instead')
# kwargs['constraints'] = ()
# NEW
C, F = [], []
for c in f:
F.append(c) if array_equal(c.lb, c.ub) else C.append(c)
kwargs['constraints'] = C
f = F
############################################
p = openopt.SNLE(f, *args, **kwargs)
if 'nlpSolver' in kwargs:
p.solver = kwargs['nlpSolver']
else: # an optimization problem
assert len(
args) > 0, 'you should have objective function as 1st argument'
objective = args[0]
if isinstance(objective, BaseFDConstraint):
raise FuncDesignerException(
"1st argument can't be of type 'FuncDesigner constraint', it should be 'FuncDesigner oofun'"
)
elif not isinstance(objective, oofun):
raise FuncDesignerException(
'1st argument should be objective function of type "FuncDesigner oofun"'
)
isLinear &= objective.getOrder(freeVars, fixedVars) < 2
if isLinear:
p = openopt.LP(*args, **kwargs)
if 'solver' not in kwargs:
for solver in self.lpSolvers:
if (':' not in solver
and not openopt.oosolver(solver).isInstalled
) or (
solver == 'glpk' and
not openopt.oosolver('cvxopt_lp').isInstalled):
continue
if solver == 'glpk':
p2 = openopt.LP([1, -1], lb=[1, 1], ub=[10, 10])
try:
r = p2.solve('glpk', iprint=-5)
except:
continue
if r.istop < 0:
continue
else:
break
if ':' in solver:
pWarn(
'You have linear problem but no linear solver (lpSolve, glpk, cvxopt_lp) is installed; converter to NLP will be used.'
)
p.solver = solver
else:
solverName = kwargs['solver']
if type(solverName) != str:
solverName = solverName.__name__
if solverName not in self.lpSolvers:
solverName = 'nlp:' + solverName
p.solver = solverName
p.warn(
'you are solving linear problem with non-linear solver'
)
else:
p = openopt.NLP(*args, **kwargs)
if 'solver' not in kwargs:
p.solver = 'ralg'
# TODO: solver autoselect
#if p.iprint >= 0: p.disp('The optimization problem is ' + p.probType)
p._isFDmodel = lambda *args, **kwargs: True
return p.solve() if kwargs.get('manage', False) in (False,
0) else p.manage()
def __solver__(self, p):
alp, h0, nh, q1, q2 = self.alp, self.h0, self.nh, self.q1, self.q2
if isPyPy:
if p.nc != 0 or p.nh != 0:
p.warn("in PyPy ralg may work incorrectly with nonlinear constraints yet")
if p.nbeq != 0 or any(p.lb==p.ub):
p.err('in PyPy ralg cannot handle linear equality constraints yet')
if type(q1) == str:
if p.probType== 'NLP' and p.isUC: q1 = 0.9
else: q1 = 1.0
T = self.T
# alternatively instead of alp=self.alp etc you can use directly self.alp etc
n = p.n
x0 = p.x0
if p.nbeq == 0 or any(abs(p._get_AeqX_eq_Beq_residuals(x0))>p.contol): # TODO: add "or Aeqconstraints(x0) out of contol"
x0[x0<p.lb] = p.lb[x0<p.lb]
x0[x0>p.ub] = p.ub[x0>p.ub]
ind_box_eq = where(p.lb==p.ub)[0]
nEQ = ind_box_eq.size
if nEQ != 0:
initLenBeq = p.nbeq
Aeq, beq, nbeq = copy(p.Aeq), copy(p.beq), p.nbeq
p.Aeq = zeros([Len(p.beq) + nEQ, p.n])
p.beq = zeros(Len(p.beq) + nEQ)
p.beq[:Len(beq)] = beq
p.Aeq[:Len(beq)] = Aeq
for i in range(len(ind_box_eq)):
p.Aeq[initLenBeq+i, ind_box_eq[i]] = 1
p.beq[initLenBeq+i] = p.lb[ind_box_eq[i]] # = p.ub[indEQ[i]], because they are the same
p.nbeq += nEQ
if not self.newLinEq or p.nbeq == 0:
needProjection = False
B0 = eye(n, dtype=T)
restoreProb = lambda *args: 0
Aeq_r, beq_r, nbeq_r = None, None, 0
else:
needProjection = True
B0 = self.getPrimevalDilationMatrixWRTlinEqConstraints(p)
#Aeq, beq, nbeq = p.Aeq, p.beq, p.nbeq
if any(abs(p._get_AeqX_eq_Beq_residuals(x0))>p.contol/16.0):
#p.debugmsg('old point Aeq residual:'+str(norm(dot(Aeq, x0)-beq)))
try:
x0 = self.linEqProjection(x0, p.Aeq, p.beq)
except LinAlgError:
s = 'Failed to obtain projection of start point to linear equality constraints subspace, probably the system is infeasible'
p.istop, p.msg = -25, s
return
#p.debugmsg('new point Aeq residual:'+str(norm(dot(Aeq, x0)-beq)))
if nEQ == 0:
Aeq_r, beq_r, nbeq_r = p.Aeq, p.beq, p.nbeq
else:
Aeq_r, beq_r, nbeq_r = Aeq, beq, nbeq
p.Aeq, p.beq, p.nbeq = None, None, 0
# TODO: return prob with unmodified Aeq, beq
def restoreProb():
p.Aeq, p.beq, p.nbeq = Aeq_r, beq_r, nbeq_r
#if nEQ != 0: restore lb, ub
b = B0.copy() if self.B is None else self.B
# B_f = diag(ones(n))
# B_constr = diag(ones(n))
hs = asarray(h0, T)
if self.innerState is not None:
hs = self.innerState['hs']
b = self.innerState['B']
ls_arr = []
w = asarray(1.0/alp-1.0, T)
""" Shor r-alg engine """
bestPoint = p.point(array(copy(x0).tolist(), T)) # tolist() for PyPy compatibility
prevIter_best_ls_point = bestPoint
prevIter_PointForDilation = bestPoint
g = bestPoint._getDirection(self.approach)
prevDirectionForDilation = g
moveDirection = g
if not any(g) and all(isfinite(g)):
# TODO: create ENUMs
p.iterfcn(bestPoint)
restoreProb()
p.istop = 14 if bestPoint.isFeas(False) else -14
p.msg = 'move direction has all-zero coords'
return
HS = []
LS = []
SwitchEncountered = False
selfNeedRej = False
doScale = False
#directionVectorsList = []
# #pass-by-ref! not copy!
# if p.isFeas(p.x0): b = B_f
# else: b = B_constr
# if p.debug and hasattr(p, 'x_opt'):
# import scipy
# exactDirection = x0-p.x_opt
# asdf_0 = exactDirection * (0.2+scipy.rand(n))
# #asdf = asdf_0.copy()
fTol = p.fTol if p.fTol is not None else 15*p.ftol
# CHANGES
if self.penalties:
oldVal = p.f(p.x0)
newVal = inf
x = p.x0
#H, DH = p.h, p.dh
if p.nh != 0:
#S = 1.0
_Aeq = p.dh(x)
_beq = -p.h(x)
df = p.df(x)
if n>=150 and not scipyInstalled:
p.pWarn(scipyAbsentMsg)
if n>100 and scipyInstalled:
from scipy.sparse import eye as Eye # to prevent numpy.eye overwrite
HH = Eye(n, n)
else:
HH = eye(n)
qp = openopt.QP(H=HH, f=df, Aeq=_Aeq, beq=_beq)
# print ('len(_beq): %d' % len(_beq))
# assert len(_beq) != 0
QPsolver = openopt.oosolver('cvxopt_qp', iprint=-1)
if not QPsolver.isInstalled:
#p.pWarn('to use ')
S = None
else:
r = qp.solve(QPsolver)
#S = 2.0*abs(r.duals).sum() if r.istop > 0 else 0
S = 10.0*sum(abs(r.duals)) if r.istop > 0 else None
while any(p.h(x)) > p.contol:
if S is not None:
p2 = getattr(openopt, p.probType)(p.f, x)
p.inspire(p2)
p2.x0 = x
p2.h = p2.dh = None
p2.userProvided.h = p2.userProvided.dh = False
p2.nh = 0
p2.f = lambda *args, **kwargs: p.f(*args, **kwargs) + sum(abs(S * p.h(*args, **kwargs)))
p2.df = lambda *args, **kwargs: p.df(*args, **kwargs) + dot(S * sign(p.h(*args, **kwargs)), p.dh(*args, **kwargs))
#p2.iterfcn = p.iterfcn
# def df2(*args, **kwargs):
# r1 = p.df(*args, **kwargs)
# r2 = S * dot(p.dh(*args, **kwargs).reshape(-1, 1), sign(p.h(*args, **kwargs))).flatten()
# #raise 0
# return r1+r2
# #p2.df = lambda *args, **kwargs: p.df(*args, **kwargs) + S * dot(p.dh(x).reshape(-1, 1), sign(p.h(*args, **kwargs))).flatten()
# p2.df = df2
# #raise 0
r2 = p2.solve(p.solver, iprint=10)
if r2.stopcase >= 0:
x = r2.xf
p.solver.innerState = r2.extras['innerState']
oldVal, newVal = newVal, r2.ff
else:
if r2.istop == IS_LINE_SEARCH_FAILED:
# TODO: custom S as raising penalties
pass
if p.isFeas(p2.xk):
p.xf = p.xk = p2.xk
p.istop, p.msg = p2.istop, p2.msg
return
else:
S *= 50
#print('max residual:%0.2e'% r2.rf)
else: # failed to solve QP
break
#print 'b:', b, '\nhs:', hs
# CHANGES END
""" Ralg main cycle """
for itn in range(p.maxIter+10):
doDilation = True
lastPointOfSameType = None # to prevent possible bugs
alp_addition = 0.0
iterStartPoint = prevIter_best_ls_point
x = iterStartPoint.x.copy()
g_tmp = economyMult(b.T, moveDirection)
if any(g_tmp): g_tmp /= p.norm(g_tmp)
g1 = p.matmult(b, g_tmp)
# norm_moveDirection = p.norm(g1)
# if doScale:
# g1 *= (norm_moveDirection_prev/norm_moveDirection) ** 0.5
# norm_moveDirection_prev = norm_moveDirection
# if p.debug and hasattr(p, 'x_opt'):
# cos_phi_0 = p.matmult(moveDirection, prevIter_best_ls_point.x - p.x_opt)/p.norm(moveDirection)/p.norm(prevIter_best_ls_point.x - p.x_opt)
# cos_phi_1 = p.matmult(g1, prevIter_best_ls_point.x - p.x_opt)/p.norm(g1)/p.norm(prevIter_best_ls_point.x - p.x_opt)
# print('beforeDilation: %f afterDilation: %f' % (cos_phi_0, cos_phi_1) )
# asdf = asdf_0.copy()
# g_tmp = economyMult(b.T, asdf)
#
# #g_tmp = p.matmult(b.T, asdf)
#
# if any(g_tmp): g_tmp /= p.norm(g_tmp)
# asdf = p.matmult(b, g_tmp)
# cos_phi = dot(asdf, exactDirection) / p.norm(asdf) / p.norm(exactDirection)
# p.debugmsg('cos_phi:%f' % cos_phi)
# assert cos_phi >0
""" Forward line search """
hs_cumsum = 0
hs_start = hs
for ls in range(p.maxLineSearch):
hs_mult = 1.0
if ls > 20:
hs_mult = 2.0
elif ls > 10:
hs_mult = 1.5
elif ls > 2:
hs_mult = 1.05
hs *= hs_mult
x -= hs * g1
hs_cumsum += hs
newPoint = p.point(x) if ls == 0 else iterStartPoint.linePoint(hs_cumsum/(hs_cumsum-hs), oldPoint) # TODO: take ls into account?
if not p.isUC:
if newPoint.isFeas(True) == iterStartPoint.isFeas(True):
lastPointOfSameType = newPoint
if self.show_nnan: p.info('ls: %d nnan: %d' % (ls, newPoint.__nnan__()))
if ls == 0:
oldPoint = prevIter_best_ls_point#prevIterPoint
oldoldPoint = oldPoint
#if not self.checkTurnByGradient:
if newPoint.betterThan(oldPoint, altLinInEq=True):
if newPoint.betterThan(bestPoint, altLinInEq=False): bestPoint = newPoint
oldoldPoint = oldPoint
oldPoint, newPoint = newPoint, None
else:
if not itn % 4:
for fn in ['_lin_ineq', '_lin_eq']:
if hasattr(newPoint, fn): delattr(newPoint, fn)
break
hs /= hs_mult
if ls == p.maxLineSearch-1:
p.istop, p.msg = IS_LINE_SEARCH_FAILED, 'maxLineSearch (' + str(p.maxLineSearch) + ') has been exceeded, the problem seems to be unbounded'
restoreProb()
return
#iterPoint = newPoint
PointForDilation = newPoint
#best_ls_point = newPoint if ls == 0 else oldPoint
#if p.debug and ls != 0: assert not oldPoint.betterThan(best_ls_point)
""" Backward line search """
mdx = max((150, 1.5*p.n))*p.xtol
if itn == 0: mdx = max((hs / 128.0, 128*p.xtol )) # TODO: set it after B rej as well
ls_backward = 0
maxLS = 3 if ls == 0 else 1
# if ls <=3 or ls > 20:
if self.doBackwardSearch:
if self.new_bs:
best_ls_point, PointForDilation, ls_backward = \
getBestPointAfterTurn(oldoldPoint, newPoint, maxLS = maxLS, maxDeltaF = 150*p.ftol, \
maxDeltaX = mdx, altLinInEq = True, new_bs = True)
if PointForDilation.isFeas(True) == iterStartPoint.isFeas(True):
lastPointOfSameType = PointForDilation
# elif best_ls_point.isFeas(altLinInEq=True) == iterStartPoint.isFeas(altLinInEq=True):
# lastPointOfSameType = best_ls_point
else:
best_ls_point, ls_backward = \
getBestPointAfterTurn(oldoldPoint, newPoint, maxLS = maxLS, altLinInEq = True, new_bs = False)
PointForDilation = best_ls_point
# TODO: extract last point from backward search, that one is better than iterPoint
if best_ls_point.betterThan(bestPoint): bestPoint = best_ls_point
#p.debugmsg('ls_backward:%d' % ls_backward)
if ls == 0 and ls_backward == -maxLS:
#pass
alp_addition += 0.25
#hs *= 0.9
if ls_backward <= -1 and itn != 0: # TODO: mb use -1 or 0 instead?
pass
#alp_addition -= 0.25*ls_backward # ls_backward less than zero
#hs *= 2 ** min((ls_backward+1, 0))
else:
pass
#hs *= 0.95
best_ls_point = PointForDilation # elseware lots of difficulties
""" Updating hs """
step_x = p.norm(PointForDilation.x - prevIter_PointForDilation.x)
step_f = abs(PointForDilation.f() - prevIter_PointForDilation.f())
HS.append(hs_start)
assert ls >= 0
LS.append(ls)
if itn > 3:
mean_ls = (3*LS[-1] + 2*LS[-2]+LS[-3]) / 6.0
j0 = 3.3
if mean_ls > j0:
hs = (mean_ls - j0 + 1)**0.5 * hs_start
else:
#hs = (ls/j0) ** 0.5 * hs_start
hs = hs_start
if ls == 0 and ls_backward == -maxLS:
shift_x = step_x / p.xtol
RD = log10(shift_x+1e-100)
if PointForDilation.isFeas(True) or prevIter_PointForDilation.isFeas(True):
RD = min((RD, asscalar(asarray(log10(step_f / p.ftol + 1e-100)))))
if RD > 1.0:
mp = (0.5, (ls/j0) ** 0.5, 1 - 0.2*RD)
hs *= max(mp)
#from numpy import argmax
#print argmax(mp), mp
""" Handling iterPoints """
best_ls_point = PointForDilation
#if not SwitchEncountered and p.nh != 0 and PointForDilation.isFeas(altLinInEq=False) != prevIter_PointForDilation.isFeas(altLinInEq=False):
#SwitchEncountered = True
#selfNeedRej = True
involve_lastPointOfSameType = False
if lastPointOfSameType is not None and PointForDilation.isFeas(True) != prevIter_PointForDilation.isFeas(True):
# TODO: add middle point for the case ls = 0
assert self.dilationType == 'plain difference'
#directionForDilation = lastPointOfSameType._getDirection(self.approach)
PointForDilation = lastPointOfSameType
involve_lastPointOfSameType = True
#directionForDilation = newPoint.__getDirection__(self.approach) # used for dilation direction obtaining
# if not self.new_bs or ls != 0:
# moveDirection = iterPoint.__getDirection__(self.approach)
# else:
# moveDirection = best_ls_point.__getDirection__(self.approach)
#directionForDilation = pointForDilation.__getDirection__(self.approach)
# cos_phi = -p.matmult(moveDirection, prevIterPoint.__getDirection__(self.approach))
# assert cos_phi.size == 1
# if cos_phi> 0:
# g2 = moveDirection#pointForDilation.__getDirection__(self.approach)
# else:
# g2 = pointForDilation.__getDirection__(self.approach)
if itn == 0:
p.debugmsg('hs: ' + str(hs))
p.debugmsg('ls: ' + str(ls))
if self.showLS: p.info('ls: ' + str(ls))
if self.show_hs: p.info('hs: ' + str(hs))
if self.show_nnan: p.info('nnan: ' + str(best_ls_point.__nnan__()))
if self.showRes:
r, fname, ind = best_ls_point.mr(True)
p.info(fname+str(ind))
""" Set dilation direction """
#if sum(p.dotmult(g, g2))>0:
#p.debugmsg('ralg warning: slope angle less than pi/2. Mb dilation for the iter will be omitted.')
#doDilation = False
# CHANGES
# if lastPointOfSameType is None:
# if currIterPointIsFeasible and not prevIterPointIsFeasible:
# alp_addition += 0.1
# elif prevIterPointIsFeasible and not currIterPointIsFeasible:
# alp_addition -= 0.0
# CHANGES END
# r_p, ind_p, fname_p = prevIter_best_ls_point.mr(1)
# r_, ind_, fname_ = PointForDilation.mr(1)
#else:
#print itn,'>>>>>>>>>', currIterPointIsFeasible
""" Excluding derivatives switched to/from NaN """
if self.skipPrevIterNaNsInDilation:
c_prev, c_current = prevIter_PointForDilation.c(), PointForDilation.c()
h_prev, h_current = prevIter_PointForDilation.h(), PointForDilation.h()
""" Handling switch to NaN """
NaN_derivatives_excluded = False
if self.skipPrevIterNaNsInDilation:
assert self.approach == 'all active'
if not prevIter_PointForDilation.isFeas(True):
""" processing NaNs in nonlin inequality constraints """
ind_switch_ineq_to_nan = where(logical_and(isnan(c_current), c_prev>0))[0]
if len(ind_switch_ineq_to_nan) != 0:
NaN_derivatives_excluded = True
tmp = prevIter_PointForDilation.dc(ind_switch_ineq_to_nan)
if hasattr(tmp, 'toarray'):
tmp = tmp.A
if len(ind_switch_ineq_to_nan)>1:
tmp *= (c_prev[ind_switch_ineq_to_nan] /sqrt((tmp**2).sum(1))).reshape(-1, 1)
else:
tmp *= c_prev[ind_switch_ineq_to_nan] / norm(tmp)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
#print '1: excluded:', norm(tmp), norm(prevDirectionForDilation)
prevDirectionForDilation -= tmp
#print '1: result=', norm(prevDirectionForDilation)
""" processing NaNs in nonlin equality constraints """
ind_switch_eq_to_nan = where(logical_and(isnan(h_current), h_prev>0))[0]
if len(ind_switch_eq_to_nan) != 0:
NaN_derivatives_excluded = True
tmp = prevIter_PointForDilation.dh(ind_switch_eq_to_nan)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
prevDirectionForDilation -= tmp
ind_switch_eq_to_nan = where(logical_and(isnan(h_current), h_prev<0))[0]
if len(ind_switch_eq_to_nan) != 0:
NaN_derivatives_excluded = True
tmp = prevIter_PointForDilation.dh(ind_switch_eq_to_nan)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
prevDirectionForDilation += tmp
directionForDilation = PointForDilation._getDirection(self.approach)
""" Handling switch from NaN """
if self.skipPrevIterNaNsInDilation:
if not PointForDilation.isFeas(True):
""" processing NaNs in nonlin inequality constraints """
ind_switch_ineq_from_nan = where(logical_and(isnan(c_prev), c_current>0))[0]
if len(ind_switch_ineq_from_nan) != 0:
NaN_derivatives_excluded = True
tmp = PointForDilation.dc(ind_switch_ineq_from_nan)
if hasattr(tmp, 'toarray'):
tmp = tmp.A
if len(ind_switch_ineq_from_nan)>1:
tmp *= (c_current[ind_switch_ineq_from_nan] /sqrt((tmp**2).sum(1))).reshape(-1, 1)
else:
tmp *= c_current[ind_switch_ineq_from_nan] / norm(tmp)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
#print '2: excluded:', norm(tmp), norm(directionForDilation)
directionForDilation -= tmp
#print '2: result=', norm(directionForDilation)
""" processing NaNs in nonlin equality constraints """
ind_switch_eq_from_nan = where(logical_and(isnan(h_prev), h_current>0))[0]
if len(ind_switch_eq_from_nan) != 0:
NaN_derivatives_excluded = True
tmp = PointForDilation.dh(ind_switch_eq_from_nan)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
directionForDilation -= tmp
ind_switch_eq_from_nan = where(logical_and(isnan(h_prev), h_current<0))[0]
if len(ind_switch_eq_from_nan) != 0:
NaN_derivatives_excluded = True
tmp = PointForDilation.dh(ind_switch_eq_from_nan)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
directionForDilation += tmp
# # CHANGES
# gn = g2/norm(g2)
# if len(directionVectorsList) == 0 or n < 3: pass
# else:
# if len(directionVectorsList) == 1 or abs(dot(directionVectorsList[-1], directionVectorsList[-2]))>0.999:
# projectionComponentLenght = abs(dot(directionVectorsList[-1], gn))
# restLength = sqrt(1 - min((1, projectionComponentLenght))**2)
# else:
# e1 = directionVectorsList[-1]
# e2 = directionVectorsList[-2] - dot(directionVectorsList[-1], directionVectorsList[-2]) * directionVectorsList[-1]
# e2 /= norm(e2)
#
# proj1, proj2 = dot(e1, gn), dot(e2, gn)
# rest = gn - proj1 * e1 - proj2 * e2
# restLength = norm(rest)
# if restLength > 1+1e-5: p.pWarn('possible error in ralg solver: incorrect restLength, exceeds 1.0')
#
# # TODO: make it parameters of ralg
# commonCoeff, alp_add_coeff = 0.5, 1.0
#
# if restLength < commonCoeff * (n - 2.0) / n:
# #pass
# alpAddition = 0.5+(arctan((n - 2.0) / (n * restLength)) - pi / 4.0) / (pi / 2.0) * alp_add_coeff
# #p.debugmsg('alpAddition:' + str(alpAddition))
# assert alpAddition > 0 # if someone incorrectly modifies commonCoeff it can be less than zero
# alp_addition += alpAddition
# #p.debugmsg('alp_addition:' + str(alp_addition))
#
# directionVectorsList.append(gn)
# if len(directionVectorsList) > 2: directionVectorsList = directionVectorsList[:-2]
# # CHANGES END
if self.dilationType == 'normalized' and (not fname_p in ('lb', 'ub', 'lin_eq', 'lin_ineq') \
or not fname_ in ('lb', 'ub', 'lin_eq', 'lin_ineq')) and (fname_p != fname_ or ind_p != ind_):
G2, G = directionForDilation/norm(directionForDilation), prevDirectionForDilation/norm(prevDirectionForDilation)
else:
G2, G = directionForDilation, prevDirectionForDilation
if prevIter_PointForDilation.isFeas(True) == PointForDilation.isFeas(True):
g1 = G2 - G
elif prevIter_PointForDilation.isFeas(True):
g1 = G2.copy()
else:
g1 = G.copy()
alp_addition += 0.05
#print p.getMaxResidual(PointForDilation.x, 1)
##############################################
# the case may be occured when
# 1) lastPointOfSameType is used
# or
# 2) some NaN from constraints have been excluded
if norm(G2 - G) < 1e-12 * min((norm(G2), norm(G))) and (involve_lastPointOfSameType or NaN_derivatives_excluded):
p.debugmsg("ralg: 'last point of same type gradient' is used")
g1 = G2
##############################################
#g1 = -G.copy() # signum doesn't matter here
# changes wrt infeas constraints
# if prevIterPoint.nNaNs() != 0:
# cp, hp = prevIterPoint.c(), prevIterPoint.h()
# ind_infeas_cp, ind_infeas_hp = isnan(cp), isnan(hp)
#
# c, h = iterPoint.c(), iterPoint.h()
# ind_infeas_c, ind_infeas_h = isnan(c), isnan(h)
#
# ind_goodChange_c = logical_and(ind_infeas_cp, logical_not(ind_infeas_c))
# ind_goodChange_h = logical_and(ind_infeas_hp, logical_not(ind_infeas_h))
#
# any_c, any_h = any(ind_goodChange_c), any(ind_goodChange_h)
# altDilation = zeros(n)
# if any_c:
# altDilation += sum(atleast_2d(iterPoint.dc(where(ind_goodChange_c)[0])), 0)
# assert not any(isnan(altDilation))
# if any_h:
# altDilation += sum(atleast_2d(iterPoint.dh(where(ind_goodChange_h)[0])), 0)
# if any_c or any_h:
# #print '!>', altDilation
# #g1 = altDilation
# pass
# changes end
""" Perform dilation """
# CHANGES
# g = economyMult(b.T, g1)
# gn = g/norm(g)
# if len(directionVectorsList) == 0 or n < 3 or norm(g1) < 1e-20: pass
# else:
# if len(directionVectorsList) == 1 or abs(dot(directionVectorsList[-1], directionVectorsList[-2]))>0.999:
# projectionComponentLenght = abs(dot(directionVectorsList[-1], gn))
# restLength = sqrt(1 - min((1, projectionComponentLenght))**2)
# else:
# e1 = directionVectorsList[-1]
# e2 = directionVectorsList[-2] - dot(directionVectorsList[-1], directionVectorsList[-2]) * directionVectorsList[-1]
# print dot(directionVectorsList[-1], directionVectorsList[-2])
# e2 /= norm(e2)
# proj1, proj2 = dot(e1, gn), dot(e2, gn)
# rest = gn - proj1 * e1 - proj2 * e2
# restLength = norm(rest)
# assert restLength < 1+1e-5, 'error in ralg solver: incorrect restLength'
#
# # TODO: make it parameters of ralg
# commonCoeff, alp_add_coeff = 0.5, 1.0
#
# if restLength < commonCoeff * (n - 2.0) / n:
# #pass
# alpAddition = 0.5+(arctan((n - 2.0) / (n * restLength)) - pi / 4.0) / (pi / 2.0) * alp_add_coeff
# #p.debugmsg('alpAddition:' + str(alpAddition))
# assert alpAddition > 0 # if someone incorrectly modifies commonCoeff it can be less than zero
# alp_addition += alpAddition
# #p.debugmsg('alp_addition:' + str(alp_addition))
#
# directionVectorsList.append(gn)
# if len(directionVectorsList) > 2: directionVectorsList = directionVectorsList[:-2]
# CHANGES END
if doDilation:
g = economyMult(b.T, g1)
ng = p.norm(g)
if self.needRej(p, b, g1, g) or selfNeedRej:
selfNeedRej = False
if self.showRej or p.debug:
p.info('debug msg: matrix B restoration in ralg solver')
b = B0.copy()
hs = p.norm(prevIter_best_ls_point.x - best_ls_point.x)
# TODO: iterPoint = projection(iterPoint,Aeq) if res_Aeq > 0.75*contol
if ng < 1e-40:
hs *= 0.9
p.debugmsg('small dilation direction norm (%e), skipping' % ng)
if all(isfinite(g)) and ng > 1e-50 and doDilation:
g = (g / ng).reshape(-1,1)
vec1 = economyMult(b, g).reshape(-1,1)# TODO: remove economyMult, use dot?
#if alp_addition != 0: p.debugmsg('alp_addition:' + str(alp_addition))
w = asarray(1.0/(alp+alp_addition)-1.0, T)
vec2 = w * g.T
b += p.matmult(vec1, vec2)
""" Call OO iterfcn """
if hasattr(p, '_df'): delattr(p, '_df')
if best_ls_point.isFeas(False) and hasattr(best_ls_point, '_df'):
p._df = best_ls_point.df().copy()
p.iterfcn(best_ls_point)
""" Check stop criteria """
cond_same_point = array_equal(best_ls_point.x, prevIter_best_ls_point.x)
if cond_same_point and not p.istop:
p.istop = 14
p.msg = 'X[k-1] and X[k] are same'
p.stopdict[SMALL_DELTA_X] = True
restoreProb()
self.innerState = {'B': b, 'hs': hs}
return
s2 = 0
if p.istop and not p.userStop:
if p.istop not in p.stopdict: p.stopdict[p.istop] = True # it's actual for converters, TODO: fix it
if SMALL_DF in p.stopdict:
if best_ls_point.isFeas(False): s2 = p.istop
p.stopdict.pop(SMALL_DF)
if SMALL_DELTA_F in p.stopdict:
# TODO: implement it more properly
if best_ls_point.isFeas(False) and prevIter_best_ls_point.f() != best_ls_point.f(): s2 = p.istop
p.stopdict.pop(SMALL_DELTA_F)
if SMALL_DELTA_X in p.stopdict:
if best_ls_point.isFeas(False) or not prevIter_best_ls_point.isFeas(False) or cond_same_point: s2 = p.istop
p.stopdict.pop(SMALL_DELTA_X)
# if s2 and (any(isnan(best_ls_point.c())) or any(isnan(best_ls_point.h()))) \
# and not p.isNaNInConstraintsAllowed\
# and not cond_same_point:
# s2 = 0
if not s2 and any(p.stopdict.values()):
for key, val in p.stopdict.items():
if val == True:
s2 = key
break
p.istop = s2
for key, val in p.stopdict.items():
if key < 0 or key in set([FVAL_IS_ENOUGH, USER_DEMAND_STOP, BUTTON_ENOUGH_HAS_BEEN_PRESSED]):
p.iterfcn(bestPoint)
self.innerState = {'B': b, 'hs': hs}
return
""" If stop required """
if p.istop:
# if self.needRej(p, b, g1, g) or not feasiblePointWasEncountered:
# b = B0.copy()
# hs = max((p.norm(prevIter_best_ls_point.x - best_ls_point.x) , 128*p.xtol))
# p.istop = 0
# else:
restoreProb()
p.iterfcn(bestPoint)
#p.istop, p.msg = istop, msg
self.innerState = {'B': b, 'hs': hs}
return
""" Some final things for ralg main cycle """
# p.debugmsg('new point Aeq residual:'+str(norm(dot(Aeq, iterPoint.x)-beq)))
# if needProjection and itn!=0:
# #pass
# x2 = self.linEqProjection(iterPoint.x, Aeq, beq)
# p.debugmsg('norm(delta):' + str(norm(iterPoint.x-x2)))
# iterPoint = p.point(x2)
# p.debugmsg('2: new point Aeq residual:'+str(norm(dot(Aeq, iterPoint.x)-beq)))
#p.hs.append(hs)
#g = moveDirection.copy()
#prevDirectionForDilation = directionForDilation
#iterPoint = None
#doScale = self.new_s and prevIter_PointForDilation.isFeas(True) != best_ls_point.isFeas(True)
#print doScale
prevIter_best_ls_point = best_ls_point
prevIter_PointForDilation = best_ls_point
prevDirectionForDilation = best_ls_point._getDirection(self.approach)
moveDirection = best_ls_point._getDirection(self.approach)
"""
The example illustrates oosolver usage
You should pay special attention for "isInstalled" field
oosolver work is untested for converters
"""
from openopt import oosolver, NLP
ipopt = oosolver('ipopt', color='r') # oosolver can hanlde prob parameters
ralg = oosolver('ralg', color='k', alp = 4.0) # as well as solver parameters
asdf = oosolver('asdf')
solvers = [ralg, asdf, ipopt]
# or just
# solvers = [oosolver('ipopt', color='r'), oosolver('asdf'), oosolver('ralg', color='k', alp = 4.0)]
for solver in solvers:
if not solver.isInstalled:
print 'solver ' + solver.__name__ + ' is not installed'
continue
p = NLP(x0 = 15, f = lambda x: x**4, df = lambda x: 4 * x**3, iprint = 0)
r = p.solve(solver, plot=1, show = solver == solvers[-1])
def solve(self, *args, **kw):
if len(args) > 1:
self.err('''
incorrect number of arguments for solve(),
must be at least 1 (solver), other must be keyword arguments''')
if self.start is None and not self.returnToStart:
self.err('for returnToStart=False mode you should provide start, other cases are unimplemented yet')
solver = args[0] if len(args) != 0 else kw.get('solver', self.solver)
KW = self.__init_kwargs.copy()
KW.update(kw)
objective = KW.get('objective', self.objective)
if isinstance(objective, (list, tuple, set)):
nCriteria = len(self.objective)
if 3 * nCriteria != np.asarray(self.objective).size:
objective = [(objective[3*i], objective[3*i+1], objective[3*i+2]) for i in range(int(round(np.asarray(self.objective).size / 3)))]
if len(objective) == 1:
KW['fTol'], KW['goal'] = objective[0][1:]
else:
objective = [(self.objective, KW.get('fTol', getattr(self, 'fTol')), KW.get('goal', getattr(self, 'goal')))]
nCriteria = len(objective)
isMOP = nCriteria > 1
mainCr = objective[0][0]
import FuncDesigner as fd, openopt as oo
solverName = solver if type(solver) == str else solver.__name__
is_interalg = solverName == 'interalg'
is_glp = solverName == 'sa'
if is_glp:
assert nCriteria == 1, 'you cannot solve multiobjective tsp by the solver'
is_interalg_raw_mode = is_interalg and KW.get('dataHandling', oo.oosolver(solver).dataHandling) in ('auto','raw')
KW.pop('objective', None)
P = oo.MOP if nCriteria > 1 else oo.GLP if is_interalg else oo.MILP if not is_glp else oo.GLP
import networkx as nx
graph = self.graph # must be networkx Graph instance
init_graph_is_directed = graph.is_directed()
init_graph_is_multigraph = graph.is_multigraph()
if not init_graph_is_multigraph or not init_graph_is_directed:
graph = nx.MultiDiGraph(graph) #if init_graph_is_directed else nx.MultiGraph(graph)
nodes = graph.nodes()
edges = graph.edges()
n = len(nodes)
m = len(edges)
node2index = dict([(node, i) for i, node in enumerate(nodes)])
# TODO: implement MOP with interalg_gdp mode (requires interpolation interval analysis for non-monotone funcs)
interalg_gdp = 1
if not is_interalg:# or isMOP:
# !!!!!!!!!!!!! TODO: add handling of MOP with interalg_gdp?
interalg_gdp = 0
if interalg_gdp:
x = []
edge_ind2x_ind_val = {}
else:
pass
#x = fd.oovars(m, domain=bool)
#cr_values = dict([(obj[0], []) for obj in objective])
cr_values = {}
constraints = []
EdgesDescriptors, EdgesCoords = [], []
# mb rework it by successors etc?
Funcs = {}
Cons = KW.pop('constraints', [])
if type(Cons) not in (list, tuple):
Cons = [Cons]
usedValues = getUsedValues(Cons)
usedValues.update(getUsedValues([obj[0] for obj in objective]))
MainCr = mainCr if type(mainCr) in (str, np.str_) else list(usedValues)[0]
isMainCrMin = objective[0][2] in ('min', 'minimum')
node_out_edges_num = []
for node in nodes:
Edges = graph[node]
node_out_edges_num.append(len(Edges))
out_nodes = Edges.keys()
if len(out_nodes) == 0:
self.err('input graph has node %s that does not lead to any other node; solution is impossible' % node)
if init_graph_is_multigraph and not isMOP and type(mainCr) in [str, np.str_]:
W = {}
for out_node in out_nodes:
ww = list(Edges[out_node].values())
for w in ww:
tmp = W.get(out_node, None)
if tmp is None:
W[out_node] = w
continue
th = tmp[mainCr]
w_main_cr_val = w[mainCr]
if isMainCrMin == (th > w_main_cr_val):
W[out_node] = w
Out_nodes, W = np.array(list(W.keys())), np.array(list(W.values()))
else:
W = np.hstack([list(Edges[out_node].values()) for out_node in out_nodes])
Out_nodes = np.hstack([[out_node] * len(Edges[out_node]) for out_node in out_nodes])
if interalg_gdp:
rr = np.array([w[MainCr] for w in W])
if isMainCrMin:
rr = -rr
elif objective[0][2] not in ('max', 'maximum'):
self.err('unimplemented for fixed value goal in TSP yet, only min/max is possible for now')
ind = rr.argsort()
W = W[ind]
Out_nodes = Out_nodes[ind]
lc = 0
for i, w in enumerate(W):
if interalg_gdp:
edge_ind2x_ind_val[len(EdgesCoords)] = (len(x), lc)
lc += 1
EdgesCoords.append((node, Out_nodes[i]))
EdgesDescriptors.append(w)
for key, val in w.items():
# for undirected:
#if node2index[key] < node2index[out_node]: continue
Val = val if self.returnToStart or node != self.start else 0
if key in cr_values:
cr_values[key].append(Val)
else:
cr_values[key] = [Val]
if interalg_gdp:
x.append(fd.oovar(domain = np.arange(lc)))
m = len(EdgesCoords) # new value
if is_glp:
if type(mainCr) not in (str, np.str_):
self.err('for the solver "sa" only text name objectives are implemented (e.g. "time", "price")')
if init_graph_is_multigraph:
self.err('the solver "sa" cannot handle multigraphs yet')
if len(Cons) != 0:
self.err('the solver "sa" cannot handle constrained TSP yet')
M = np.empty((n, n))
M.fill(np.nan)
Cr_values = np.array(cr_values[mainCr])
isMax = objective[0][-1] in ('max', 'maximum')
if isMax:
Cr_values = -Cr_values
for i, w in enumerate(EdgesDescriptors):
node_in, node_out = EdgesCoords[i]
M[node_in, node_out] = Cr_values[i]
S = np.abs(Cr_values).sum() + 1.0
# TODO: check it
M[np.isnan(M)] = S
prob = P(lambda x: 0, np.zeros(n), iprint = 1)
prob.f = lambda x: np.nan if not hasattr(prob, 'ff') else (prob.ff if isMax else -prob.ff)
prob.M = dict([((i, j), M[i, j]) for i in range(n) for j in range(n) if i != j])
r = prob.solve(solver, **KW)
xf = [nodes[j] for j in np.array(r.xf, int)]
r.nodes = xf#.tolist()
if self.start is not None:
j = r.nodes.index(self.start)
r.nodes = r.nodes[j:] + r.nodes[:j]
if self.returnToStart:
r.nodes += [r.nodes[0]]
r.edges = [(r.nodes[i], r.nodes[i+1]) for i in range(n-1)]
r.Edges = [(r.nodes[i], r.nodes[i+1], graph[r.nodes[i]][r.nodes[i+1]][0]) for i in range(n-1)]
if self.returnToStart:
r.edges.append((r.nodes[-2], r.nodes[0]))
print(r.nodes[-1], r.nodes[0], type(r.nodes[-1]), type(r.nodes[0]), graph[2])
r.Edges.append((r.nodes[-2], r.nodes[0], graph[r.nodes[-2]][r.nodes[0]][0]))
#r.xf = r.xk = r.nodes
# TODO: Edges
return r
#TODO: fix ooarray lb/ub
#u = np.array([1] + [fd.oovar(lb=2, ub=n) for i in range(n-1)])
u = fd.hstack((1, fd.oovars(n-1, lb=2, ub=n)))
for i in range(1, u.size):
u[i]('u' + str(i))
if is_interalg_raw_mode:
for i in range(n-1):
u[1+i].domain = np.arange(2, n+1)
if interalg_gdp:
assert len(x) == n
x = fd.ooarray(x)
# TODO: remove it when proper enum implementation in FD engine will be done
for i in range(n):
x[i]._init_domain = x[i].domain
constraints.append(x[i]-x[i]._init_domain[-1] <= 0)
x[i].domain = np.arange(int(2 ** np.ceil(np.log2(node_out_edges_num[i]))))
# for i in range(n-1):
# u[1+i]._init_domain = u[1+i].domain
# constraints.append(u[1+i]-u[1+i]._init_domain[-1] <= 0)
# u[1+i].domain = np.arange(u[1+i]._init_domain[0], u[1+i]._init_domain[0]+int(2 ** np.ceil(np.log2(u[1+i]._init_domain[-1]-u[1+i]._init_domain[0]+1))))
# u[1+i].ub = u[1+i].domain[-1]
else:
x = fd.oovars(m, domain=bool) # new m value
for i in range(x.size):
x[i]('x'+str(i))
# if init_graph_is_directed:
dictFrom = dict([(node, []) for node in nodes])
dictTo = dict([(node, []) for node in nodes])
for i, edge in enumerate(EdgesCoords):
From, To = edge
dictFrom[From].append(i)
dictTo[To].append(i)
engine = fd.XOR
# number of outcoming edges = 1
if not interalg_gdp:
for node, edges_inds in dictFrom.items():
# !!!!!!!!!! TODO for interalg_raw_mode: and if all edges have sign similar to goal
if 1 and is_interalg_raw_mode:
c = engine([x[j] for j in edges_inds])
else:
nEdges = fd.sum([x[j] for j in edges_inds])
c = nEdges >= 1 if self.allowRevisit else nEdges == 1
constraints.append(c)
# number of incoming edges = 1
for node, edges_inds in dictTo.items():
if len(edges_inds) == 0:
self.err('input graph has node %s that has no edge from any other node; solution is impossible' % node)
if interalg_gdp:
x_inds, x_vals = [], []
for elem in edges_inds:
x_ind, x_val = edge_ind2x_ind_val[elem]
x_inds.append(x_ind)
x_vals.append(x_val)
c = engine([(x[x_ind] == x_val)(tol = 0.5) for x_ind, x_val in zip(x_inds, x_vals)])
else:
if 1 and is_interalg_raw_mode and engine == fd.XOR:
c = engine([x[j] for j in edges_inds])
else:
nEdges = fd.sum([x[j] for j in edges_inds])
c = nEdges >= 1 if self.allowRevisit else nEdges == 1
constraints.append(c)
# MTZ
for i, (I, J) in enumerate(EdgesCoords):
ii, jj = node2index[I], node2index[J]
if ii != 0 and jj != 0:
if interalg_gdp:
x_ind, x_val = edge_ind2x_ind_val[i]
c = fd.ifThen((x[x_ind] == x_val)(tol=0.5), u[ii] - u[jj] <= - 1.0)
elif is_interalg_raw_mode:
c = fd.ifThen(x[i], u[ii] - u[jj] <= - 1.0)#u[jj] - u[ii] >= 1)
else:
c = u[ii] - u[jj] + 1 <= (n-1) * (1-x[i])
constraints.append(c)
# handling objective(s)
FF = []
for optCrName in usedValues:
tmp = cr_values.get(optCrName, [])
if len(tmp) == 0:
self.err('seems like graph edgs have no attribute "%s" to perform optimization on it' % optCrName)
elif len(tmp) != m:
self.err('for optimization creterion "%s" at least one edge has no this attribute' % optCrName)
if interalg_gdp:
F = []
lc = 0
for X in x:
domain = X._init_domain
vals = [tmp[i] for i in range(lc, lc + domain.size)]
lc += domain.size
#F = sum(x)
#F.append(fd.interpolator(domain, vals, k=1, s=0.00000001)(X))
F.append(fd.interpolator(domain, vals, k=1)(X))
#print(domain, vals)
F = fd.sum(F)
else:
F = fd.sum(x*tmp)
Funcs[optCrName] = F
for obj in objective:
FF.append((Funcs[obj[0]] if type(obj[0]) in (str, np.str_) else obj[0](Funcs), obj[1], obj[2]))
for c in Cons:
tmp = c(Funcs)
if type(tmp) in (list, tuple, set):
constraints += list(tmp)
else:
constraints.append(tmp)
startPoint = {x:[0]*(m if not interalg_gdp else n)}
startPoint.update(dict([(U, i+2) for i, U in enumerate(u[1:])]))
p = P(FF if isMOP else FF[0][0], startPoint, constraints = constraints)#, fixedVars = fixedVars)
for param in ('start', 'returnToStart'):
KW.pop(param, None)
r = p.solve(solver, **KW)
if P != oo.MOP:
r.ff = p.ff
if interalg_gdp:
x_ind_val2edge_ind = dict([(elem[1], elem[0]) for elem in edge_ind2x_ind_val.items()])
SolutionEdges = [(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i]) for i in [x_ind_val2edge_ind[(ind, x[ind](r))] for ind in range(n)]]
else:
SolutionEdges = [(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i]) for i in range(m) if r.xf[x[i]] == 1]
if len(SolutionEdges) == 0:
r.nodes = r.edges = r.Edges = []
return r
S = dict([(elem[0], elem) for elem in SolutionEdges])
SE = [SolutionEdges[0]]
for i in range(len(SolutionEdges)-1):
SE.append(S[SE[-1][1]])
SolutionEdgesCoords = [(elem[0], elem[1]) for elem in SE]
nodes = [edge[1] for edge in SolutionEdgesCoords]
if self.start is not None:
shift_ind = nodes.index(self.start)
nodes = nodes[shift_ind:] + nodes[:shift_ind]
if self.returnToStart:
nodes.append(nodes[0])
edges = SolutionEdgesCoords[1:] + [SolutionEdgesCoords[0]]
Edges = SE[1:] + [SE[0]]
if self.start is not None:
edges, Edges = edges[shift_ind:] + edges[:shift_ind], Edges[shift_ind:] + Edges[:shift_ind]
if not self.returnToStart:
edges, Edges = edges[:-1], Edges[:-1]
r.nodes, r.edges, r.Edges = nodes, edges, Edges
else:
r.solution = 'for MOP see r.solutions instead of r.solution'
tmp_c, tmp_v = r.solutions.coords, r.solutions.values
# if interalg_gdp:
# x_ind_val2edge_ind = dict([(elem[1], elem[0]) for elem in edge_ind2x_ind_val.items()])
# SolutionEdges = [(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i]) for i in [x_ind_val2edge_ind[(ind, x[ind](r))] for ind in range(n)]]
# else:
# SolutionEdges = [(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i]) for i in range(m) if r.xf[x[i]] == 1]
if interalg_gdp: # default for MOP
x_ind_val2edge_ind = dict([(elem[1], elem[0]) for elem in edge_ind2x_ind_val.items()])
r.solutions = MOPsolutions([[(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i]) for i in [x_ind_val2edge_ind[(ind, x[ind](Point))] for ind in range(n)]] for Point in r.solutions])
else:# non-default
r.solutions = MOPsolutions([[(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i]) for i in range(m) if Point[x[i]] == 1] for Point in r.solutions])
r.solutions.values = tmp_v
return r
def cb(p):
tmp = ceil(log10(norm(1.0 - p.xk)))
if tmp < cb.TMP:
# print 'distance:', tmp, 'itn:', p.iter, 'n_func:', p.nEvals['f'], 'n_grad:', -p.nEvals['df']
cb.TMP = tmp
cb.stat['dist'].append(tmp)
cb.stat['f'].append(p.nEvals['f'])
cb.stat['df'].append(-p.nEvals['df'])
return False
asa = lambda x: asarray(x).reshape(-1, 1)
solvers = ['ralg', 'amsg2p', 'gsubg']
solvers = [oosolver('amsg2p', gamma=2.0)]
Colors = ['r', 'k', 'b']
lines = []
R = {}
for Tol_p in range(-10, -31, -1):
#print('Tol = 10^%d' % Tol_p)
for i, solver in enumerate(solvers):
p = NSP(obj,
startPoint,
maxIter=1700,
name='Rzhevsky6 (nVars: ' + str(n) + ')',
maxTime=300,
maxFunEvals=1e7,
color=Colors[i])
p.fOpt = 0.0
p.fTol = 10**Tol_p
def solve(self, *args, **kw):
if len(args) > 1:
self.err('''
incorrect number of arguments for solve(),
must be at least 1 (solver), other must be keyword arguments''')
if self.start is None and not self.returnToStart:
self.err(
'for returnToStart=False mode you should provide start, other cases are unimplemented yet'
)
solver = args[0] if len(args) != 0 else kw.get('solver', self.solver)
KW = self.__init_kwargs.copy()
KW.update(kw)
objective = KW.get('objective', self.objective)
if isinstance(objective, (list, tuple, set)):
nCriteria = len(self.objective)
if 3 * nCriteria != np.asarray(self.objective).size:
objective = [
(objective[3 * i], objective[3 * i + 1],
objective[3 * i + 2]) for i in range(
int(round(np.asarray(self.objective).size / 3)))
]
if len(objective) == 1:
KW['fTol'], KW['goal'] = objective[0][1:]
else:
objective = [(self.objective, KW.get('fTol', getattr(self,
'fTol')),
KW.get('goal', getattr(self, 'goal')))]
nCriteria = len(objective)
isMOP = nCriteria > 1
mainCr = objective[0][0]
import FuncDesigner as fd, openopt as oo
solverName = solver if type(solver) == str else solver.__name__
is_interalg = solverName == 'interalg'
is_glp = solverName == 'sa'
if is_glp:
assert nCriteria == 1, 'you cannot solve multiobjective tsp by the solver'
is_interalg_raw_mode = is_interalg and KW.get(
'dataHandling',
oo.oosolver(solver).dataHandling) in ('auto', 'raw')
KW.pop('objective', None)
P = oo.MOP if nCriteria > 1 else oo.GLP if is_interalg else oo.MILP if not is_glp else oo.GLP
import networkx as nx
graph = self.graph # must be networkx Graph instance
init_graph_is_directed = graph.is_directed()
init_graph_is_multigraph = graph.is_multigraph()
if not init_graph_is_multigraph or not init_graph_is_directed:
graph = nx.MultiDiGraph(
graph) #if init_graph_is_directed else nx.MultiGraph(graph)
nodes = graph.nodes()
edges = graph.edges()
n = len(nodes)
m = len(edges)
node2index = dict([(node, i) for i, node in enumerate(nodes)])
# TODO: implement MOP with interalg_gdp mode (requires interpolation interval analysis for non-monotone funcs)
interalg_gdp = 1
if not is_interalg: # or isMOP:
# !!!!!!!!!!!!! TODO: add handling of MOP with interalg_gdp?
interalg_gdp = 0
if interalg_gdp:
x = []
edge_ind2x_ind_val = {}
else:
pass
#x = fd.oovars(m, domain=bool)
#cr_values = dict([(obj[0], []) for obj in objective])
cr_values = {}
constraints = []
EdgesDescriptors, EdgesCoords = [], []
# mb rework it by successors etc?
Funcs = {}
Cons = KW.pop('constraints', [])
if type(Cons) not in (list, tuple):
Cons = [Cons]
usedValues = getUsedValues(Cons)
usedValues.update(getUsedValues([obj[0] for obj in objective]))
MainCr = mainCr if type(mainCr) in (str,
np.str_) else list(usedValues)[0]
isMainCrMin = objective[0][2] in ('min', 'minimum')
node_out_edges_num = []
for node in nodes:
Edges = graph[node]
node_out_edges_num.append(len(Edges))
out_nodes = Edges.keys()
if len(out_nodes) == 0:
self.err(
'input graph has node %s that does not lead to any other node; solution is impossible'
% node)
if init_graph_is_multigraph and not isMOP and type(mainCr) in [
str, np.str_
]:
W = {}
for out_node in out_nodes:
ww = list(Edges[out_node].values())
for w in ww:
tmp = W.get(out_node, None)
if tmp is None:
W[out_node] = w
continue
th = tmp[mainCr]
w_main_cr_val = w[mainCr]
if isMainCrMin == (th > w_main_cr_val):
W[out_node] = w
Out_nodes, W = np.array(list(W.keys())), np.array(
list(W.values()))
else:
W = np.hstack(
[list(Edges[out_node].values()) for out_node in out_nodes])
Out_nodes = np.hstack([[out_node] * len(Edges[out_node])
for out_node in out_nodes])
if interalg_gdp:
rr = np.array([w[MainCr] for w in W])
if isMainCrMin:
rr = -rr
elif objective[0][2] not in ('max', 'maximum'):
self.err(
'unimplemented for fixed value goal in TSP yet, only min/max is possible for now'
)
ind = rr.argsort()
W = W[ind]
Out_nodes = Out_nodes[ind]
lc = 0
for i, w in enumerate(W):
if interalg_gdp:
edge_ind2x_ind_val[len(EdgesCoords)] = (len(x), lc)
lc += 1
EdgesCoords.append((node, Out_nodes[i]))
EdgesDescriptors.append(w)
for key, val in w.items():
# for undirected:
#if node2index[key] < node2index[out_node]: continue
Val = val if self.returnToStart or node != self.start else 0
if key in cr_values:
cr_values[key].append(Val)
else:
cr_values[key] = [Val]
if interalg_gdp:
x.append(fd.oovar(domain=np.arange(lc)))
m = len(EdgesCoords) # new value
if is_glp:
if type(mainCr) not in (str, np.str_):
self.err(
'for the solver "sa" only text name objectives are implemented (e.g. "time", "price")'
)
if init_graph_is_multigraph:
self.err('the solver "sa" cannot handle multigraphs yet')
if len(Cons) != 0:
self.err('the solver "sa" cannot handle constrained TSP yet')
M = np.empty((n, n))
M.fill(np.nan)
Cr_values = np.array(cr_values[mainCr])
isMax = objective[0][-1] in ('max', 'maximum')
if isMax:
Cr_values = -Cr_values
for i, w in enumerate(EdgesDescriptors):
node_in, node_out = EdgesCoords[i]
M[node_in, node_out] = Cr_values[i]
S = np.abs(Cr_values).sum() + 1.0
# TODO: check it
M[np.isnan(M)] = S
prob = P(lambda x: 0, np.zeros(n), iprint=1)
prob.f = lambda x: np.nan if not hasattr(prob, 'ff') else (
prob.ff if isMax else -prob.ff)
prob.M = dict([((i, j), M[i, j]) for i in range(n)
for j in range(n) if i != j])
r = prob.solve(solver, **KW)
xf = [nodes[j] for j in np.array(r.xf, int)]
r.nodes = xf #.tolist()
if self.start is not None:
j = r.nodes.index(self.start)
r.nodes = r.nodes[j:] + r.nodes[:j]
if self.returnToStart:
r.nodes += [r.nodes[0]]
r.edges = [(r.nodes[i], r.nodes[i + 1]) for i in range(n - 1)]
r.Edges = [(r.nodes[i], r.nodes[i + 1],
graph[r.nodes[i]][r.nodes[i + 1]][0])
for i in range(n - 1)]
if self.returnToStart:
r.edges.append((r.nodes[-2], r.nodes[0]))
print(r.nodes[-1], r.nodes[0], type(r.nodes[-1]),
type(r.nodes[0]), graph[2])
r.Edges.append((r.nodes[-2], r.nodes[0],
graph[r.nodes[-2]][r.nodes[0]][0]))
#r.xf = r.xk = r.nodes
# TODO: Edges
return r
#TODO: fix ooarray lb/ub
#u = np.array([1] + [fd.oovar(lb=2, ub=n) for i in range(n-1)])
u = fd.hstack((1, fd.oovars(n - 1, lb=2, ub=n)))
for i in range(1, u.size):
u[i]('u' + str(i))
if is_interalg_raw_mode:
for i in range(n - 1):
u[1 + i].domain = np.arange(2, n + 1)
if interalg_gdp:
assert len(x) == n
x = fd.ooarray(x)
# TODO: remove it when proper enum implementation in FD engine will be done
for i in range(n):
x[i]._init_domain = x[i].domain
constraints.append(x[i] - x[i]._init_domain[-1] <= 0)
x[i].domain = np.arange(
int(2**np.ceil(np.log2(node_out_edges_num[i]))))
# for i in range(n-1):
# u[1+i]._init_domain = u[1+i].domain
# constraints.append(u[1+i]-u[1+i]._init_domain[-1] <= 0)
# u[1+i].domain = np.arange(u[1+i]._init_domain[0], u[1+i]._init_domain[0]+int(2 ** np.ceil(np.log2(u[1+i]._init_domain[-1]-u[1+i]._init_domain[0]+1))))
# u[1+i].ub = u[1+i].domain[-1]
else:
x = fd.oovars(m, domain=bool) # new m value
for i in range(x.size):
x[i]('x' + str(i))
# if init_graph_is_directed:
dictFrom = dict([(node, []) for node in nodes])
dictTo = dict([(node, []) for node in nodes])
for i, edge in enumerate(EdgesCoords):
From, To = edge
dictFrom[From].append(i)
dictTo[To].append(i)
engine = fd.XOR
# number of outcoming edges = 1
if not interalg_gdp:
for node, edges_inds in dictFrom.items():
# !!!!!!!!!! TODO for interalg_raw_mode: and if all edges have sign similar to goal
if 1 and is_interalg_raw_mode:
c = engine([x[j] for j in edges_inds])
else:
nEdges = fd.sum([x[j] for j in edges_inds])
c = nEdges >= 1 if self.allowRevisit else nEdges == 1
constraints.append(c)
# number of incoming edges = 1
for node, edges_inds in dictTo.items():
if len(edges_inds) == 0:
self.err(
'input graph has node %s that has no edge from any other node; solution is impossible'
% node)
if interalg_gdp:
x_inds, x_vals = [], []
for elem in edges_inds:
x_ind, x_val = edge_ind2x_ind_val[elem]
x_inds.append(x_ind)
x_vals.append(x_val)
c = engine([(x[x_ind] == x_val)(tol=0.5)
for x_ind, x_val in zip(x_inds, x_vals)])
else:
if 1 and is_interalg_raw_mode and engine == fd.XOR:
c = engine([x[j] for j in edges_inds])
else:
nEdges = fd.sum([x[j] for j in edges_inds])
c = nEdges >= 1 if self.allowRevisit else nEdges == 1
constraints.append(c)
# MTZ
for i, (I, J) in enumerate(EdgesCoords):
ii, jj = node2index[I], node2index[J]
if ii != 0 and jj != 0:
if interalg_gdp:
x_ind, x_val = edge_ind2x_ind_val[i]
c = fd.ifThen((x[x_ind] == x_val)(tol=0.5),
u[ii] - u[jj] <= -1.0)
elif is_interalg_raw_mode:
c = fd.ifThen(x[i],
u[ii] - u[jj] <= -1.0) #u[jj] - u[ii] >= 1)
else:
c = u[ii] - u[jj] + 1 <= (n - 1) * (1 - x[i])
constraints.append(c)
# handling objective(s)
FF = []
for optCrName in usedValues:
tmp = cr_values.get(optCrName, [])
if len(tmp) == 0:
self.err(
'seems like graph edgs have no attribute "%s" to perform optimization on it'
% optCrName)
elif len(tmp) != m:
self.err(
'for optimization creterion "%s" at least one edge has no this attribute'
% optCrName)
if interalg_gdp:
F = []
lc = 0
for X in x:
domain = X._init_domain
vals = [tmp[i] for i in range(lc, lc + domain.size)]
lc += domain.size
#F = sum(x)
#F.append(fd.interpolator(domain, vals, k=1, s=0.00000001)(X))
F.append(fd.interpolator(domain, vals, k=1)(X))
#print(domain, vals)
F = fd.sum(F)
else:
F = fd.sum(x * tmp)
Funcs[optCrName] = F
for obj in objective:
FF.append(
(Funcs[obj[0]] if type(obj[0]) in (str,
np.str_) else obj[0](Funcs),
obj[1], obj[2]))
for c in Cons:
tmp = c(Funcs)
if type(tmp) in (list, tuple, set):
constraints += list(tmp)
else:
constraints.append(tmp)
startPoint = {x: [0] * (m if not interalg_gdp else n)}
startPoint.update(dict([(U, i + 2) for i, U in enumerate(u[1:])]))
p = P(FF if isMOP else FF[0][0], startPoint,
constraints=constraints) #, fixedVars = fixedVars)
for param in ('start', 'returnToStart'):
KW.pop(param, None)
r = p.solve(solver, **KW)
if P != oo.MOP:
r.ff = p.ff
if interalg_gdp:
x_ind_val2edge_ind = dict([
(elem[1], elem[0]) for elem in edge_ind2x_ind_val.items()
])
SolutionEdges = [
(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i])
for i in
[x_ind_val2edge_ind[(ind, x[ind](r))] for ind in range(n)]
]
else:
SolutionEdges = [(EdgesCoords[i][0], EdgesCoords[i][1],
EdgesDescriptors[i]) for i in range(m)
if r.xf[x[i]] == 1]
if len(SolutionEdges) == 0:
r.nodes = r.edges = r.Edges = []
return r
S = dict([(elem[0], elem) for elem in SolutionEdges])
SE = [SolutionEdges[0]]
for i in range(len(SolutionEdges) - 1):
SE.append(S[SE[-1][1]])
SolutionEdgesCoords = [(elem[0], elem[1]) for elem in SE]
nodes = [edge[1] for edge in SolutionEdgesCoords]
if self.start is not None:
shift_ind = nodes.index(self.start)
nodes = nodes[shift_ind:] + nodes[:shift_ind]
if self.returnToStart:
nodes.append(nodes[0])
edges = SolutionEdgesCoords[1:] + [SolutionEdgesCoords[0]]
Edges = SE[1:] + [SE[0]]
if self.start is not None:
edges, Edges = edges[shift_ind:] + edges[:shift_ind], Edges[
shift_ind:] + Edges[:shift_ind]
if not self.returnToStart:
edges, Edges = edges[:-1], Edges[:-1]
r.nodes, r.edges, r.Edges = nodes, edges, Edges
else:
r.solution = 'for MOP see r.solutions instead of r.solution'
tmp_c, tmp_v = r.solutions.coords, r.solutions.values
# if interalg_gdp:
# x_ind_val2edge_ind = dict([(elem[1], elem[0]) for elem in edge_ind2x_ind_val.items()])
# SolutionEdges = [(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i]) for i in [x_ind_val2edge_ind[(ind, x[ind](r))] for ind in range(n)]]
# else:
# SolutionEdges = [(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i]) for i in range(m) if r.xf[x[i]] == 1]
if interalg_gdp: # default for MOP
x_ind_val2edge_ind = dict([
(elem[1], elem[0]) for elem in edge_ind2x_ind_val.items()
])
r.solutions = MOPsolutions([[
(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i])
for i in [
x_ind_val2edge_ind[(ind, x[ind](Point))]
for ind in range(n)
]
] for Point in r.solutions])
else: # non-default
r.solutions = MOPsolutions([[
(EdgesCoords[i][0], EdgesCoords[i][1], EdgesDescriptors[i])
for i in range(m) if Point[x[i]] == 1
] for Point in r.solutions])
r.solutions.values = tmp_v
return r
from numpy import arange
from numpy.linalg import norm
from openopt import NSP, oosolver
from FuncDesigner import *
N = 300
x = oovar('x')
startPoint = {x: 1 + 1.0 / arange(1, N)}
S = 1e4 ** (1.0/arange(1, N))
#f = abs(x[0]) + S * abs(x[1]) + S**2 * abs(x[2])
f = sum(abs(x)*S)
solvers = [oosolver('ralg')]
solvers = [oosolver('gsubg', addASG = True)]
#solvers = [oosolver('gsubg', zhurb = 20, dual=False)]
#solvers = ['ipopt']
#solvers = ['slmvm2']
#solvers = ['mma']
for solver in solvers:
p = NSP(f, startPoint, maxIter = 10000, maxTime = 15000, maxFunEvals=1e7)
p.fEnough = 1.5e1
p.fTol = 1.0e1
#p.constraints = (y > 5)(tol=1e-4) #x>1e-1 #[2*y<sin(arange(N))]
#r = p.solve(solver, iprint=10, xtol = 1e-36, ftol = 1e-16, show = solver == solvers[-1])
r = p.solve(solver, iprint=10, xtol = 1e-16, ftol = 1e-6, show = solver == solvers[-1])
# or
#f2 = Max([5*abs(x[0])+1, 50*abs(x[1])+2, 500*x[2]**2+4])
#startPoint = {x:100 + cos(arange(3)), y:cos(arange(N))}
#----------------------------
f = f1 + f2
print 'start point: f1 = %e f2 = %e' % (f1(startPoint), f2(startPoint))
#print "start point: norm(f1') = %e norm(f2') = %e" % (norm(f1.D(startPoint, y)), norm(f2.D(startPoint, x)))
ralg = oosolver('ralg')
gsubg = oosolver('gsubg', addASG = False)
solvers = [ralg]
#solvers = [oosolver('gsubg', zhurb = 20, dual=False)]
solvers = ['ipopt', gsubg, 'scipy_cg']
solvers = [gsubg]
#solvers = ['ipopt']
#solvers = ['slmvm2']
#solvers = ['slmvm1']
#solvers = ['mma']
Colors = ['r', 'k','b']
lines = []
for i, solver in enumerate(solvers):
obj = sum(tmp)
startPoint = {x: 2*ones(n)}
def cb(p):
tmp = ceil(log10(norm(1.0 - p.xk)))
if tmp < cb.TMP:
# print 'distance:', tmp, 'itn:', p.iter, 'n_func:', p.nEvals['f'], 'n_grad:', -p.nEvals['df']
cb.TMP = tmp
cb.stat['dist'].append(tmp)
cb.stat['f'].append(p.nEvals['f'])
cb.stat['df'].append(-p.nEvals['df'])
return False
asa = lambda x:asarray(x).reshape(-1, 1)
solvers = ['ralg', 'amsg2p', 'gsubg']
solvers = [oosolver('amsg2p', gamma = 2.0)]
Colors = ['r', 'k','b']
lines = []
R = {}
for Tol_p in range(-10, -31, -1):
#print('Tol = 10^%d' % Tol_p)
for i, solver in enumerate(solvers):
p = NSP(obj, startPoint, maxIter = 1700, name = 'Rzhevsky6 (nVars: ' + str(n)+')', maxTime = 300, maxFunEvals=1e7, color = Colors[i])
p.fOpt = 0.0
p.fTol = 10**Tol_p
cb.TMP = 1000
cb.stat = {'dist':[], 'f':[], 'df':[]}
r = p.solve(solver, iprint=-1, xtol = 0, ftol = 0, callback = cb)
R[solver] = hstack((asa(cb.stat['dist']), asa(cb.stat['f']), asa(cb.stat['df'])))
print('itn df dx %d %0.1e %0.1e' % (-r.evals['df'], r.ff-p.fOpt, norm(p.xk-1.0)))
# print('objective evals: %d gradient evals: %d ' % (r.evals['f'],r.evals['df']))
"""
The example illustrates oosolver usage
You should pay special attention for "isInstalled" field
oosolver work is untested for converters
"""
from openopt import oosolver, NLP
ipopt = oosolver('ipopt', color='r') # oosolver can hanlde prob parameters
ralg = oosolver('ralg', color='k', alp=4.0) # as well as solver parameters
asdf = oosolver('asdf')
solvers = [ralg, asdf, ipopt]
# or just
# solvers = [oosolver('ipopt', color='r'), oosolver('asdf'), oosolver('ralg', color='k', alp = 4.0)]
for solver in solvers:
if not solver.isInstalled:
print 'solver ' + solver.__name__ + ' is not installed'
continue
p = NLP(x0=15, f=lambda x: x**4, df=lambda x: 4 * x**3, iprint=0)
r = p.solve(solver, plot=1, show=solver == solvers[-1])
from numpy import arange
from numpy.linalg import norm
from openopt import NSP, oosolver
from FuncDesigner import *
N = 100000
x = oovar('x')
startPoint = {x: 1 + 1.0 / arange(1, N+1)}
S = 1e4 ** (1.0/arange(1, N+1))
arr = sin(arange(N))
f = sum((x-arr)**2 * S) / 1e4
solvers = [oosolver('ralg')]
solvers = [oosolver('gsubg', dual=True, zhurb = 50)]
#solvers = [oosolver('gsubg', zhurb = 20, dual=False)]
#solvers = ['ipopt']
#solvers = ['slmvm2']
#solvers = ['mma']
for solver in solvers:
p = NSP(f, startPoint, maxIter = 10000, maxTime = 15000, maxFunEvals=1e7)
p.fEnough = 1.5e-1
p.fTol = 5.0e-1
#p.constraints = (y > 5)(tol=1e-4) #x>1e-1 #[2*y<sin(arange(N))]
#r = p.solve(solver, iprint=10, xtol = 1e-36, ftol = 1e-16, show = solver == solvers[-1])
r = p.manage(solver, iprint=1, xtol = 1e-8, ftol = 1e-7, show = solver == solvers[-1])
def startOptimization(self, root, varsRoot, AddVar, currValues, \
ValsColumnName, ObjEntry, ExperimentNumber, Next, NN, goal, objtol, C):
AddVar.destroy()
ValsColumnName.set('Experiment parameters')
n = len(self.NameEntriesList)
Names, Lb, Ub, Tol, x0 = [], [], [], [], []
for i in range(n):
N, L, U, T, valEntry = \
self.NameEntriesList[i], self.LB_EntriesList[i], self.UB_EntriesList[i], self.TolEntriesList[i], self.ValueEntriesList[i]
N.config(state=DISABLED)
L.config(state=DISABLED)
U.config(state=DISABLED)
T.config(state=DISABLED)
#valEntry.config(state=DISABLED)
name, lb, ub, tol, val = N.get(), L.get(), U.get(), T.get(
), valEntry.get()
Names.append(name)
x0.append(float(val))
Lb.append(float(lb) if lb != '' else -inf)
Ub.append(float(ub) if ub != '' else inf)
# TODO: fix zero
Tol.append(float(tol) if tol != '' else 0)
x0, Tol, Lb, Ub = asfarray(x0), asfarray(Tol), asfarray(Lb), asfarray(
Ub)
x0 *= xtolScaleFactor / Tol
#self.x0 = copy(x0)
from openopt import NLP, oosolver
p = NLP(objective,
x0,
lb=Lb * xtolScaleFactor / Tol,
ub=Ub * xtolScaleFactor / Tol)
self.prob = p
#calculated_points = [(copy(x0), copy(float(ObjEntry.get())))
p.args = (Tol, self, ObjEntry, p, root, ExperimentNumber, Next, NN,
objtol, C)
#p.graphics.rate = -inf
#p.f_iter = 2
solver = oosolver('bobyqa', useStopByException=False)
p.solve(solver, iprint=1, goal=goal) #, plot=1, xlabel='nf')
self.solved = True
if p.stopcase >= 0:
self.ValsColumnName.set('Best parameters')
NN.set('Best obtained objective value:')
#Next.config(state=DISABLED)
Next.destroy()
#reverse = True if goal == 'min' else False
calculated_items = self.calculated_points.items() if isinstance(
self.calculated_points, dict) else self.calculated_points
vals = [calculated_items[i][1] for i in range(len(calculated_items))]
ind = argsort(vals)
j = ind[0] if goal == 'min' else ind[-1]
key, val = calculated_items[j]
text_coords = key.split(' ')
for i in range(len(self.ValueEntriesList)):
self.ValueEntriesList[i].delete(0, END)
self.ValueEntriesList[i].insert(0, text_coords[i])
ObjEntry.delete(0, END)
obj_tol = self.ObjTolEntry.get()
val = float(val) * 1e4 * objtol
ObjEntry.insert(0, str(val))
ObjEntry.config(state=DISABLED)
def _solve(self, *args, **kwargs):
#!!!!! TODO: determing LP, MILP, MINLP if possible
try:
import openopt
except ImportError:
raise FuncDesignerException('to perform the operation you should have openopt installed')
constraints = self._getAllConstraints()
# TODO: mention it in doc
if 'constraints' in kwargs:
tmp = set(kwargs['constraints'])
tmp.update(set(constraints))
kwargs['constraints'] = tmp
else:
kwargs['constraints'] = constraints
freeVars, fixedVars = kwargs.get('freeVars', None), kwargs.get('fixedVars', None)
isSystemOfEquations = kwargs['goal'] == 'solution'
isLinear = all([(c.oofun.getOrder(freeVars, fixedVars) < 2) for c in constraints])
if isSystemOfEquations:
if isLinear: # Linear equations system
p = sle(list(kwargs['constraints']), *args, **kwargs)
else: # Nonlinear equations system
f = kwargs['constraints']
############################################
#cons = self._getAllConstraints()
# OLD
# if not all([array_equal(c.lb, c.ub) for c in f]):
# raise FuncDesignerException('Solving constrained nonlinear systems is not implemented for oosystem yet, use SNLE constructor instead')
# kwargs['constraints'] = ()
# NEW
C, F = [], []
for c in f:
F.append(c) if array_equal(c.lb, c.ub) else C.append(c)
kwargs['constraints'] = C
f = F
############################################
p = openopt.SNLE(f, *args, **kwargs)
if 'nlpSolver' in kwargs:
p.solver = kwargs['nlpSolver']
else: # an optimization problem
assert len(args) > 0, 'you should have objective function as 1st argument'
objective = args[0]
if isinstance(objective, BaseFDConstraint):
raise FuncDesignerException("1st argument can't be of type 'FuncDesigner constraint', it should be 'FuncDesigner oofun'")
elif not isinstance(objective, oofun):
raise FuncDesignerException('1st argument should be objective function of type "FuncDesigner oofun"')
isLinear &= objective.getOrder(freeVars, fixedVars) < 2
if isLinear:
p = openopt.LP(*args, **kwargs)
if 'solver' not in kwargs:
for solver in self.lpSolvers:
if (':' not in solver and not openopt.oosolver(solver).isInstalled )or (solver == 'glpk' and not openopt.oosolver('cvxopt_lp').isInstalled):
continue
if solver == 'glpk' :
p2 = openopt.LP([1, -1], lb = [1, 1], ub=[10, 10])
try:
r = p2.solve('glpk', iprint=-5)
except:
continue
if r.istop < 0:
continue
else:
break
if ':' in solver:
pWarn('You have linear problem but no linear solver (lpSolve, glpk, cvxopt_lp) is installed; converter to NLP will be used.')
p.solver = solver
else:
solverName = kwargs['solver']
if type(solverName) != str:
solverName = solverName.__name__
if solverName not in self.lpSolvers:
solverName = 'nlp:' + solverName
p.solver = solverName
p.warn('you are solving linear problem with non-linear solver')
else:
p = openopt.NLP(*args, **kwargs)
if 'solver' not in kwargs:
p.solver = 'ralg'
# TODO: solver autoselect
#if p.iprint >= 0: p.disp('The optimization problem is ' + p.probType)
p._isFDmodel = lambda *args, **kwargs: True
return p.solve() if kwargs.get('manage', False) in (False, 0) else p.manage()
def set_routine(p, *args, **kw):
if len(args) > 1:
p.err('''
incorrect number of arguments for solve(),
must be at least 1 (solver), other must be keyword arguments''')
solver = args[0] if len(args) != 0 else kw.get('solver', p.solver)
import FuncDesigner as fd, openopt
is_interalg = openopt.oosolver(solver).__name__ == 'interalg'
graph = p.graph # must be networkx instance
nodes = graph.nodes()
edges = graph.edges()
n = len(nodes)
node2index = dict((node, i) for i, node in enumerate(nodes))
index2node = dict((i, node) for i, node in enumerate(nodes))
x = fd.oovars(n, domain=bool)
objective = fd.sum(x)
startPoint = {x:[0]*n}
fixedVars = {}
includedNodes = getattr(kw, 'includedNodes', None)
if includedNodes is None:
includedNodes = getattr(p, 'includedNodes', ())
for node in includedNodes:
fixedVars[x[node2index[node]]] = 1
excludedNodes = getattr(kw, 'excludedNodes', None)
if excludedNodes is None:
excludedNodes = getattr(p, 'excludedNodes', ())
for node in excludedNodes:
fixedVars[x[node2index[node]]] = 0
if p.probType == 'DSP':
constraints = []
engine = fd.OR if is_interalg else lambda List: fd.sum(List) >= 1
Engine = lambda d, n: engine([x[node2index[k]] for k in (list(d.keys())+[n])])
for node in nodes:
adjacent_nodes_dict = graph[node]
if len(adjacent_nodes_dict) == 0:
fixedVars[x[node2index[node]]] = 1
continue
constraints.append(Engine(adjacent_nodes_dict, node))
else:
constraints = \
[fd.NAND(x[node2index[i]], x[node2index[j]]) for i, j in edges] \
if is_interalg else \
[x[node2index[i]]+x[node2index[j]] <=1 for i, j in edges]
P = openopt.GLP if is_interalg else openopt.MILP
goal = 'min' if p.probType == 'DSP' else 'max'
p = P(objective, startPoint, constraints = constraints, fixedVars = fixedVars, goal = goal)
for key, val in kw.items():
setattr(p, key, val)
r = p.solve(solver, **kw)
r.solution = [index2node[i] for i in range(n) if r.xf[x[i]] == 1]
r.ff = len(r.solution)
return r
from FuncDesigner import *
from openopt import NLP, oosolver
from numpy import arange
N = 10
a = oovar('a', size=N)
b = oovar('b', size=N)
f = (sum(abs(a*arange(1,N+1))**2.5)+sum(abs(b*arange(1,N+1))**1.5)) / 10000
startPoint = {a:cos(arange(N)), b:sin(arange(N))} # however, you'd better use numpy arrays instead of Python lists
p = NLP(f, startPoint)
#p.constraints = [(a>1)(tol=1e-5), a**2+b**2 < 0.1+a+b]# + [(a[i]**2+b[i]**2 < 3+abs(a[i])+abs(b[i])) for i in range(N)]
p.constraints = [a>1, 10*a>10] # + [(a[i]**2+b[i]**2 < 3+abs(a[i])+abs(b[i])) for i in range(N)]
#p.constraints = []
#C = sum(a**2+b**2 - (0.1+a+b)
C = [ifThenElse(a[i]**2+b[i]**2 - (0.1+a[i]+b[i])<0, 0, a[i]**2+b[i]**2 - (0.1+a[i]+b[i])) for i in range(N)]
p.constraints.append(sum(C)<0)
solver = 'ipopt'
solver = oosolver('ralg')
#solver = oosolver('ralg', approach='nqp')
r = p.minimize(solver, maxIter = 15000)