def CVXOPT_SOCP_Solver(p, solverName):
if solverName == 'native_CVXOPT_SOCP_Solver': solverName = None
cvxopt_solvers.options['maxiters'] = p.maxIter
cvxopt_solvers.options['feastol'] = p.contol
cvxopt_solvers.options['abstol'] = p.ftol
if p.iprint <= 0:
cvxopt_solvers.options['show_progress'] = False
cvxopt_solvers.options['LPX_K_MSGLEV'] = 0
cvxopt_solvers.options['MSK_IPAR_LOG'] = 0
xBounds2Matrix(p)
#FIXME: if problem is search for MAXIMUM, not MINIMUM!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
f = copy(p.f).reshape(-1,1)
# CVXOPT has some problems with x0 so currently I decided to avoid using the one
Gq, hq = [], []
C, d, q, s = p.C, p.d, p.q, p.s
for i in range(len(q)):
Gq.append(Matrix(Vstack((-atleast_1d(q[i]),-atleast_1d(C[i]) if not isspmatrix(C[i]) else C[i]))))
hq.append(matrix(hstack((atleast_1d(s[i]), atleast_1d(d[i]))), tc='d'))
sol = cvxopt_solvers.socp(Matrix(p.f), Gl=Matrix(p.A), hl = Matrix(p.b), Gq=Gq, hq=hq, A=Matrix(p.Aeq), b=Matrix(p.beq), solver=solverName)
p.msg = sol['status']
if p.msg == 'optimal' : p.istop = SOLVED_WITH_UNIMPLEMENTED_OR_UNKNOWN_REASON
else: p.istop = -100
if sol['x'] is not None:
p.xf = asarray(sol['x']).flatten()
p.ff = sum(p.dotmult(p.f, p.xf))
else:
p.ff = nan
p.xf = nan*ones(p.n)
def CVXOPT_LP_Solver(p, solverName):
#os.close(1); os.close(2)
if solverName == 'native_CVXOPT_LP_Solver': solverName = None
cvxopt_solvers.options['maxiters'] = p.maxIter
cvxopt_solvers.options['feastol'] = p.contol
cvxopt_solvers.options['abstol'] = p.ftol
if p.iprint <= 0:
cvxopt_solvers.options['show_progress'] = False
cvxopt_solvers.options['LPX_K_MSGLEV'] = 0
cvxopt_solvers.options['MSK_IPAR_LOG'] = 0
xBounds2Matrix(p)
#WholeRepr2LinConst(p)
# CVXOPT have some problems with x0 so currently I decided to avoid using the one
#if p.x0.size>0 and p.x0.flatten()[0] != None and all(isfinite(p.x0)):
# sol= cvxopt_solvers.solvers.lp(Matrix(p.f), Matrix(p.A), Matrix(p.b), Matrix(p.Aeq), Matrix(p.beq), solverName)
#else:
if (len(p.intVars)>0 or len(p.binVars)>0) and solverName == 'glpk':
from cvxopt.glpk import ilp
c = Matrix(p.f)
A, b = Matrix(p.Aeq), Matrix(p.beq)
G, h = Matrix(p.A), Matrix(p.b)
if A is None:
A = matrix(0.0, (0, p.n))
b = matrix(0.0,(0,1))
if G is None:
G = matrix(0.0, (0, p.n))
h = matrix(0.0,(0,1))
(status, x) = ilp(c, G, h, A, b, set(p.intVars), B=set(p.binVars))
if status == 'optimal': p.istop = SOLVED_WITH_UNIMPLEMENTED_OR_UNKNOWN_REASON
elif status == 'maxiters exceeded': p.istop = IS_MAX_ITER_REACHED
elif status == 'time limit exceeded': p.istop = IS_MAX_TIME_REACHED
elif status == 'unknown': p.istop = UNDEFINED
else: p.istop = FAILED_WITH_UNIMPLEMENTED_OR_UNKNOWN_REASON
if x is None:
p.xf = nan*ones(p.n)
else:
p.xf = array(x).flatten()#w/o flatten it yields incorrect result in ff!
p.ff = sum(p.dotmult(p.f, p.xf))
p.msg = status
else:
# if len(p.b) != 0:
# s0 = matrix(p.b - dot(p.A, p.x0))
# else:
# s0 = matrix()
# primalstart = {'x': matrix(p.x0), 's': s0}
# sol = cvxopt_solvers.lp(Matrix(p.f), Matrix(p.A), Matrix(p.b), Matrix(p.Aeq), Matrix(p.beq), solverName, primalstart)
sol = cvxopt_solvers.lp(Matrix(p.f), Matrix(p.A), Matrix(p.b), Matrix(p.Aeq), Matrix(p.beq), solverName)
p.msg = sol['status']
if p.msg == 'optimal' : p.istop = SOLVED_WITH_UNIMPLEMENTED_OR_UNKNOWN_REASON
else: p.istop = -100
if sol['x'] is not None:
p.xf = asarray(sol['x']).flatten()
# ! don't involve p.ff - it can be different because of goal and additional constant from FuncDesigner
p.duals = concatenate((asarray(sol['y']).flatten(), asarray(sol['z']).flatten()))
else:
p.ff = nan
p.xf = nan*ones(p.n)
def CVXOPT_QP_Solver(p, solverName):
if solverName == 'native_CVXOPT_QP_Solver': solverName = None
cvxopt_solvers.options['maxiters'] = p.maxIter
cvxopt_solvers.options['feastol'] = p.contol
cvxopt_solvers.options['abstol'] = p.ftol
cvxopt_solvers.options['reltol'] = 1e-16
if p.iprint <= 0:
cvxopt_solvers.options['show_progress'] = False
cvxopt_solvers.options['MSK_IPAR_LOG'] = 0
xBounds2Matrix(p)
f = copy(p.f).reshape(-1, 1)
sol = cvxopt_solvers.qp(Matrix(p.H), Matrix(p.f), Matrix(p.A), Matrix(p.b),
Matrix(p.Aeq), Matrix(p.beq), solverName)
p.msg = sol['status']
if p.msg == 'optimal': p.istop = 1000
else: p.istop = -100
if sol['x'] is not None:
p.xf = xf = asfarray(sol['x']).flatten()
p.ff = asfarray(0.5 * dot(xf, p.matMultVec(p.H, xf)) +
p.dotmult(p.f, xf).sum()).flatten()
p.duals = concatenate(
(asfarray(sol['y']).flatten(), asfarray(sol['z']).flatten()))
else:
p.ff = nan
p.xf = nan * ones(p.n)
def CVXOPT_QP_Solver(p, solverName):
if solverName == 'native_CVXOPT_QP_Solver': solverName = None
cvxopt_solvers.options['maxiters'] = p.maxIter
cvxopt_solvers.options['feastol'] = p.contol
cvxopt_solvers.options['abstol'] = p.ftol
cvxopt_solvers.options['reltol'] = 1e-16
if p.iprint <= 0:
cvxopt_solvers.options['show_progress'] = False
cvxopt_solvers.options['MSK_IPAR_LOG'] = 0
xBounds2Matrix(p)
f = copy(p.f).reshape(-1,1)
sol = cvxopt_solvers.qp(Matrix(p.H), Matrix(p.f), Matrix(p.A), Matrix(p.b), Matrix(p.Aeq), Matrix(p.beq), solverName)
p.msg = sol['status']
if p.msg == 'optimal' : p.istop = 1000
else: p.istop = -100
if sol['x'] is not None:
p.xf = xf = asfarray(sol['x']).flatten()
p.ff = asfarray(0.5*dot(xf, p.matMultVec(p.H, xf)) + p.dotmult(p.f, xf).sum()).flatten()
p.duals = concatenate((asfarray(sol['y']).flatten(), asfarray(sol['z']).flatten()))
else:
p.ff = nan
p.xf = nan*ones(p.n)
def CVXOPT_SDP_Solver(p, solverName):
if solverName == 'native_CVXOPT_SDP_Solver': solverName = None
cvxopt_solvers.options['maxiters'] = p.maxIter
cvxopt_solvers.options['feastol'] = p.contol
cvxopt_solvers.options['abstol'] = p.ftol
if p.iprint <= 0:
cvxopt_solvers.options['show_progress'] = False
cvxopt_solvers.options['LPX_K_MSGLEV'] = 0
#cvxopt_solvers.options['MSK_IPAR_LOG'] = 0
xBounds2Matrix(p)
#FIXME: if problem is search for MAXIMUM, not MINIMUM!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
f = copy(p.f).reshape(-1, 1)
# CVXOPT have some problems with x0 so currently I decided to avoid using the one
sol = cvxopt_solvers.sdp(Matrix(p.f), Matrix(p.A), Matrix(p.b), \
converter_to_CVXOPT_SDP_Matrices_from_OO_SDP_Class(p.S, p.n), \
DictToList(p.d), Matrix(p.Aeq), Matrix(p.beq), solverName)
p.msg = sol['status']
if p.msg == 'optimal':
p.istop = SOLVED_WITH_UNIMPLEMENTED_OR_UNKNOWN_REASON
else:
p.istop = -100
if sol['x'] is not None:
p.xf = asarray(sol['x']).flatten()
p.ff = sum(p.dotmult(p.f, p.xf))
#p.duals = concatenate((asarray(sol['y']).flatten(), asarray(sol['z']).flatten()))
else:
p.ff = nan
p.xf = nan * ones(p.n)
def CVXOPT_SDP_Solver(p, solverName):
if solverName == 'native_CVXOPT_SDP_Solver': solverName = None
cvxopt_solvers.options['maxiters'] = p.maxIter
cvxopt_solvers.options['feastol'] = p.contol
cvxopt_solvers.options['abstol'] = p.ftol
if p.iprint <= 0:
cvxopt_solvers.options['show_progress'] = False
cvxopt_solvers.options['LPX_K_MSGLEV'] = 0
#cvxopt_solvers.options['MSK_IPAR_LOG'] = 0
xBounds2Matrix(p)
#FIXME: if problem is search for MAXIMUM, not MINIMUM!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
f = copy(p.f).reshape(-1,1)
# CVXOPT have some problems with x0 so currently I decided to avoid using the one
sol = cvxopt_solvers.sdp(Matrix(p.f), Matrix(p.A), Matrix(p.b), \
converter_to_CVXOPT_SDP_Matrices_from_OO_SDP_Class(p.S, p.n), \
DictToList(p.d), Matrix(p.Aeq), Matrix(p.beq), solverName)
p.msg = sol['status']
if p.msg == 'optimal' : p.istop = SOLVED_WITH_UNIMPLEMENTED_OR_UNKNOWN_REASON
else: p.istop = -100
if sol['x'] is not None:
p.xf = asarray(sol['x']).flatten()
p.ff = sum(p.dotmult(p.f, p.xf))
#p.duals = concatenate((asarray(sol['y']).flatten(), asarray(sol['z']).flatten()))
else:
p.ff = nan
p.xf = nan*ones(p.n)
def __solver__(self, p):
#p.kernelIterFuncs.pop(SMALL_DELTA_X)
#p.kernelIterFuncs.pop(SMALL_DELTA_F)
xBounds2Matrix(p)
p.cobyla = EmptyClass()
if p.userProvided.c: p.cobyla.nc = p.c(p.x0).size
else: p.cobyla.nc = 0
if p.userProvided.h: p.cobyla.nh = p.h(p.x0).size
else: p.cobyla.nh = 0
det_arr = cumsum(array((p.cobyla.nc, p.cobyla.nh, p.b.size, p.beq.size, p.cobyla.nh, p.beq.size)))
cons = []
for i in range(det_arr[-1]):
if i < det_arr[0]:
c = lambda x, i=i: - p.c(x)[i] # cobyla requires positive constraints!
elif det_arr[0] <= i < det_arr[1]:
j = i - det_arr[0]
c = lambda x, j=j: p.h(x)[j]
elif det_arr[1] <= i < det_arr[2]:
j = i - det_arr[1]
#assert 0<= j <p.cobyla.nb
c = lambda x, j=j: p.b[j] - p.dotmult(p.A[j], x).sum() # cobyla requires positive constraints!
elif det_arr[2] <= i < det_arr[3]:
j = i - det_arr[2]
#assert 0<= j <p.cobyla.nbeq
c = lambda x, j=j: p.dotmult(p.Aeq[j], x).sum() - p.beq[j]
elif det_arr[3] <= i < det_arr[4]:
j = i - det_arr[3]
c = lambda x, j=j: - p.h(x)[j]
elif det_arr[4] <= i < det_arr[5]:
j = i - det_arr[4]
#assert 0<= j <p.cobyla.nbeq
c = lambda x, j=j: p.dotmult(p.Aeq[j], x).sum() - p.beq[j]
else:
p.err('error in connection cobyla to openopt')
cons.append(c)
## def oo_cobyla_cons(x):
## c0 = -p.c(x)
## c1 = p.h(x)
## c2 = -(p.matmult(p.A, x) - p.b)
## c3 = p.matmult(p.Aeq, x) - p.beq
## return hstack((c0, c1, -c1, c2, c3, -c3))
# p.xk = p.x0.copy()
# p.fk = p.f(p.x0)
#
# p.iterfcn()
# if p.istop:
# p.xf = p.xk
# p.ff = p.fk
# return
xf = fmin_cobyla(p.f, p.x0, cons = tuple(cons), iprint = 0, maxfun = p.maxFunEvals, rhoend = p.xtol )
p.xk = xf
p.fk = p.f(xf)
p.istop = 1000
def CVXOPT_SOCP_Solver(p, solverName):
if solverName == 'native_CVXOPT_SOCP_Solver': solverName = None
cvxopt_solvers.options['maxiters'] = p.maxIter
cvxopt_solvers.options['feastol'] = p.contol
cvxopt_solvers.options['abstol'] = p.ftol
if p.iprint <= 0:
cvxopt_solvers.options['show_progress'] = False
cvxopt_solvers.options['LPX_K_MSGLEV'] = 0
cvxopt_solvers.options['MSK_IPAR_LOG'] = 0
xBounds2Matrix(p)
#FIXME: if problem is search for MAXIMUM, not MINIMUM!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
f = copy(p.f).reshape(-1, 1)
# CVXOPT has some problems with x0 so currently I decided to avoid using the one
Gq, hq = [], []
C, d, q, s = p.C, p.d, p.q, p.s
for i in range(len(q)):
Gq.append(
Matrix(
Vstack((-atleast_1d(q[i]),
-atleast_1d(C[i]) if not isspmatrix(C[i]) else C[i]))))
hq.append(matrix(hstack((atleast_1d(s[i]), atleast_1d(d[i]))), tc='d'))
sol = cvxopt_solvers.socp(Matrix(p.f),
Gl=Matrix(p.A),
hl=Matrix(p.b),
Gq=Gq,
hq=hq,
A=Matrix(p.Aeq),
b=Matrix(p.beq),
solver=solverName)
p.msg = sol['status']
if p.msg == 'optimal':
p.istop = SOLVED_WITH_UNIMPLEMENTED_OR_UNKNOWN_REASON
else:
p.istop = -100
if sol['x'] is not None:
p.xf = asarray(sol['x']).flatten()
p.ff = sum(p.dotmult(p.f, p.xf))
else:
p.ff = nan
p.xf = nan * ones(p.n)
def __solver__(self, p):
xBounds2Matrix(p)
n = p.n
Ind_unbounded = logical_and(isinf(p.lb), isinf(p.ub))
ind_unbounded = where(Ind_unbounded)[0]
n_unbounded = ind_unbounded.size
# Cast linear inequality constraints into linear equality constraints using slack variables
nLinInEq, nLinEq = p.b.size, p.beq.size
#p.lb, p.ub = empty(n+nLinInEq+n_unbounded), empty(n+nLinInEq+n_unbounded)
#p.lb.fill(-inf)
#p.ub.fill(inf)
# for fn in ['_A', '_Aeq']:
# if hasattr(p, fn): delattr(p, fn)
# TODO: handle p.useSparse parameter
# if n_unbounded != 0:
# R = SparseMatrixConstructor((n_unbounded, n))
# R[range(n_unbounded), ind_unbounded] = 1.0
# R2 = Diag([1]*nLinInEq+[-1]*n_unbounded)
# _A = Hstack((Vstack((p.A, R)), R2))
# else:
# _A = Hstack((p.A, Eye(nLinInEq)))
_A = Hstack((p.A, Eye(nLinInEq), -Vstack([p.A[:, i] for i in ind_unbounded]).T if isPyPy else -p.A[:, ind_unbounded]))
# if isspmatrix(_A):
# _A = _A.A
# add lin eq cons
if nLinEq != 0:
Constructor = SparseMatrixConstructor if scipyInstalled and nLinInEq > 100 else DenseMatrixConstructor
_A = Vstack((_A, Hstack((p.Aeq, Constructor((nLinEq, nLinInEq)), \
-Vstack([p.Aeq[:, i] for i in ind_unbounded]).T if isPyPy else -p.Aeq[:, ind_unbounded]))))
if isspmatrix(_A):
if _A.size > 0.3 * prod(_A.shape):
_A = _A.A
else:
_A = _A.tolil()
#_A = _A.A
#p.A, p.b = zeros((0, p.n)), array([])
#_f = hstack((p.f, zeros(nLinInEq+n_unbounded)))
#_f = Hstack((p.f, zeros(nLinInEq), -p.f[ind_unbounded]))
#_b = hstack((p.b, [0]*n_unbounded, p.beq))
_f = hstack((p.f, zeros(nLinInEq), -p.f[Ind_unbounded]))
_b = hstack((p.b, p.beq))
if p.useSparse is False and isspmatrix(_A): _A = _A.A
p.debugmsg('handling as sparse: ' + str(isspmatrix(_A)))
optx,zmin,is_bounded,sol,basis = lp_engine(_f, _A, _b)
p.xf = optx[:n] -optx[-n:] if len(optx) != 0 else nan
p.istop = 1000 if p.xf is not nan else -1000
def __solver__(self, p):
xBounds2Matrix(p)
n = p.n
Ind_unbounded = logical_and(isinf(p.lb), isinf(p.ub))
ind_unbounded = where(Ind_unbounded)[0]
n_unbounded = ind_unbounded.size
# Cast linear inequality constraints into linear equality constraints using slack variables
nLinInEq, nLinEq = p.b.size, p.beq.size
#p.lb, p.ub = empty(n+nLinInEq+n_unbounded), empty(n+nLinInEq+n_unbounded)
#p.lb.fill(-inf)
#p.ub.fill(inf)
# for fn in ['_A', '_Aeq']:
# if hasattr(p, fn): delattr(p, fn)
# TODO: handle p.useSparse parameter
# if n_unbounded != 0:
# R = SparseMatrixConstructor((n_unbounded, n))
# R[range(n_unbounded), ind_unbounded] = 1.0
# R2 = Diag([1]*nLinInEq+[-1]*n_unbounded)
# _A = Hstack((Vstack((p.A, R)), R2))
# else:
# _A = Hstack((p.A, Eye(nLinInEq)))
_A = Hstack(
(p.A, Eye(nLinInEq), -Vstack([p.A[:, i] for i in ind_unbounded]).T
if isPyPy else -p.A[:, ind_unbounded]))
# if isspmatrix(_A):
# _A = _A.A
# add lin eq cons
if nLinEq != 0:
Constructor = SparseMatrixConstructor if scipyInstalled and nLinInEq > 100 else DenseMatrixConstructor
_A = Vstack((_A, Hstack((p.Aeq, Constructor((nLinEq, nLinInEq)), \
-Vstack([p.Aeq[:, i] for i in ind_unbounded]).T if isPyPy else -p.Aeq[:, ind_unbounded]))))
if isspmatrix(_A):
if _A.size > 0.3 * prod(_A.shape):
_A = _A.A
else:
_A = _A.tolil()
#_A = _A.A
#p.A, p.b = zeros((0, p.n)), array([])
#_f = hstack((p.f, zeros(nLinInEq+n_unbounded)))
#_f = Hstack((p.f, zeros(nLinInEq), -p.f[ind_unbounded]))
#_b = hstack((p.b, [0]*n_unbounded, p.beq))
_f = hstack((p.f, zeros(nLinInEq), -p.f[Ind_unbounded]))
_b = hstack((p.b, p.beq))
if p.useSparse is False and isspmatrix(_A): _A = _A.A
p.debugmsg('handling as sparse: ' + str(isspmatrix(_A)))
optx, zmin, is_bounded, sol, basis = lp_engine(_f, _A, _b)
p.xf = optx[:n] - optx[-n:] if len(optx) != 0 else nan
p.istop = 1000 if p.xf is not nan else -1000
