def invert(self, results, inverse_data):
# model = results["model"]
attr = {}
if s.SOLVE_TIME in results:
attr[s.SOLVE_TIME] = results[s.SOLVE_TIME]
attr[s.NUM_ITERS] = \
int(results['bariter']) \
if not inverse_data[XPRESS.IS_MIP] \
else 0
status_map_lp, status_map_mip = get_status_maps()
if results['status'] == 'solver_error':
status = 'solver_error'
elif 'mip_' in results['getProbStatusString']:
status = status_map_mip[results['status']]
else:
status = status_map_lp[results['status']]
if status in s.SOLUTION_PRESENT:
# Get objective value
opt_val = results['getObjVal'] + inverse_data[s.OFFSET]
# Get solution
x = np.array(results['getSolution'])
primal_vars = {
XPRESS.VAR_ID:
intf.DEFAULT_INTF.const_to_matrix(np.array(x))
}
# Only add duals if not a MIP.
dual_vars = None
if not inverse_data[XPRESS.IS_MIP]:
y = -np.array(results['getDual'])
dual_vars = {XPRESS.DUAL_VAR_ID: y}
else:
primal_vars = None
dual_vars = None
opt_val = np.inf
if status == s.UNBOUNDED:
opt_val = -np.inf
return Solution(status, opt_val, primal_vars, dual_vars, attr)
def solve_via_data(self,
data,
warm_start: bool,
verbose: bool,
solver_opts,
solver_cache=None):
import xpress as xp
# Objective function: 1/2 x' P x + q'x
Q = data[s.P] # objective quadratic coefficients
q = data[s.Q] # objective linear coefficient (size n_var)
# Equations, Ax = b
A = data[s.A] # linear coefficient matrix
b = data[s.B] # rhs
n_var = data['n_var']
n_eq = data['n_eq']
self.prob_ = xp.problem()
# qp_solver has the following format:
#
# minimize 1/2 x' P x + q' x
# subject to A x = b
# F x <= g
#
# Instead of combining A and F to call loadproblem() once
# (which is inefficient due to a necessary Python loop), call
# loadproblem() for A and then use addrow()
mstart = makeMstart(A, n_var, 1)
if len(Q.data) != 0:
# Q matrix is input via row/col indices and value, but only
# for the upper triangle. We just make it symmetric and twice
# itself, then, just remove all lower-triangular elements.
Q += Q.transpose()
Q /= 2
Q = Q.tocoo()
mqcol1 = Q.row[Q.row <= Q.col]
mqcol2 = Q.col[Q.row <= Q.col]
dqe = Q.data[Q.row <= Q.col]
else:
mqcol1, mqcol2, dqe = [], [], []
colnames = ['x_{0:09d}'.format(i) for i in range(n_var)]
rownames = ['eq_{0:09d}'.format(i) for i in range(n_eq)]
if verbose:
self.prob_.controls.miplog = 2
self.prob_.controls.lplog = 1
self.prob_.controls.outputlog = 1
else:
self.prob_.controls.miplog = 0
self.prob_.controls.lplog = 0
self.prob_.controls.outputlog = 0
self.prob_.controls.xslp_log = -1
self.prob_.loadproblem(
probname='CVX_xpress_qp',
# constraint types
qrtypes=['E'] * n_eq,
rhs=b, # rhs
range=None, # range
obj=q, # obj coeff
mstart=mstart, # mstart
mnel=None, # mnel (unused)
# linear coefficients
mrwind=A.indices[A.data != 0], # row indices
dmatval=A.data[A.data != 0], # coefficients
dlb=[-xp.infinity] * len(q), # lower bound
dub=[xp.infinity] * len(q), # upper bound
# quadratic objective (only upper triangle)
mqcol1=mqcol1,
mqcol2=mqcol2,
dqe=dqe,
# binary and integer variables
qgtype=['B'] * len(data[s.BOOL_IDX]) +
['I'] * len(data[s.INT_IDX]),
mgcols=data[s.BOOL_IDX] + data[s.INT_IDX],
# variables' and constraints' names
colnames=colnames,
rownames=rownames)
# The problem currently has the quadratic objective function
# and the linear equations. Add the linear inequalities
#
# Fx <= g
n_ineq = data['n_ineq']
if n_ineq > 0:
F = data[s.F].tocsr(
) # linear coefficient matrix, converted to row-major
g = data[s.G] # rhs
mstartIneq = makeMstart(F, n_ineq, 0) # ifCol=0 --> check rows
rownames_ineq = ['ineq_{0:09d}'.format(i) for i in range(n_ineq)]
self.prob_.addrows( # constraint types
qrtype=['L'] * n_ineq, # inequalities sign
rhs=g, # rhs
mstart=mstartIneq, # starting indices
mclind=F.indices[F.data != 0], # column indices
dmatval=F.data[F.data != 0], # coefficient
names=rownames_ineq) # row names
# Set options
#
# The parameter solver_opts is a dictionary that contains only
# one key, 'solver_opt', and its value is a dictionary
# {'control': value}, matching perfectly the format used by
# the Xpress Python interface.
# Set options if compatible with Xpress problem control names
self.prob_.setControl({
i: solver_opts[i]
for i in solver_opts if i in xp.controls.__dict__
})
if 'bargaptarget' not in solver_opts.keys():
self.prob_.controls.bargaptarget = 1e-30
if 'feastol' not in solver_opts.keys():
self.prob_.controls.feastol = 1e-9
# Solve problem
results_dict = {"model": self.prob_}
try:
# If option given, write file before solving
if 'write_mps' in solver_opts.keys():
self.prob_.write(solver_opts['write_mps'])
self.prob_.solve()
results_dict[s.SOLVE_TIME] = self.prob_.attributes.time
except xp.SolverError: # Error in the solution
results_dict["status"] = s.SOLVER_ERROR
else:
results_dict['status'] = self.prob_.getProbStatus()
results_dict[
'getProbStatusString'] = self.prob_.getProbStatusString()
results_dict['obj_value'] = self.prob_.getObjVal()
try:
results_dict[s.PRIMAL] = np.array(self.prob_.getSolution())
except xp.SolverError:
results_dict[s.PRIMAL] = np.zeros(self.prob_.attributes.ncol)
status_map_lp, status_map_mip = get_status_maps()
if results_dict['status'] == 'solver_error':
status = 'solver_error'
elif 'mip_' in results_dict['getProbStatusString']:
status = status_map_mip[results_dict['status']]
else:
status = status_map_lp[results_dict['status']]
results_dict['bariter'] = self.prob_.attributes.bariter
results_dict[
'getProbStatusString'] = self.prob_.getProbStatusString()
if status in s.SOLUTION_PRESENT:
results_dict['getObjVal'] = self.prob_.getObjVal()
results_dict['getSolution'] = self.prob_.getSolution()
if not (data[s.BOOL_IDX] or data[s.INT_IDX]):
results_dict['getDual'] = self.prob_.getDual()
del self.prob_
return results_dict
def solve_via_data(self,
data,
warm_start,
verbose,
solver_opts,
solver_cache=None):
import xpress as xp
# Objective function: 1/2 x' P x + q'x
Q = data[s.P] # objective quadratic coefficients
q = data[s.Q] # objective linear coefficient (size n_var)
# Equations, Ax = b
A = data[s.A] # linear coefficient matrix
b = data[s.B] # rhs
# Inequalities, Fx <= g
F = data[s.F] # linear coefficient matrix
g = data[s.G] # rhs
n_var = data['n_var']
n_eq = data['n_eq']
n_ineq = data['n_ineq']
self.prob_ = xp.problem()
# qp_solver has the following format:
#
# minimize 1/2 x' P x + q' x
# subject to A x = b
# F x <= g
mstartA = makeMstart(A, n_var, 1)
mstartF = makeMstart(F, n_var, 1)
# index start is simply the sum of the two mstarts for A and F
mstart = mstartA + mstartF
# In order to arrange (A;F) as a single matrix, we have to
# concatenate all indices. I prefer not to do a vstack of A
# and F as it might increase memory usage a lot.
# Begin by creating two vectors of zeros for indices and
# coefficients
mrwind = np.zeros(mstart[n_var], dtype=np.int64)
dmatval = np.zeros(mstart[n_var], dtype=np.float64)
# Fill in mrwind and dmatval by alternately drawing from
# indices/coeff of A and F.
for i in range(n_var):
nElemA = mstartA[i + 1] - mstartA[
i] # number of elements of A in this column
nElemF = mstartF[i + 1] - mstartF[
i] # number of elements of F in this column
if nElemA:
mrwind[mstart[i]:mstart[i] + nElemA] = \
A.indices[A.data != 0][mstartA[i]:mstartA[i+1]]
dmatval[mstart[i]:mstart[i] + nElemA] = \
A.data[A.data != 0][mstartA[i]:mstartA[i+1]]
if nElemF:
mrwind[mstart[i] + nElemA: mstart[i] + nElemA + nElemF] = \
n_eq + F.indices[F.data != 0][mstartF[i]:mstartF[i+1]]
dmatval[mstart[i] + nElemA: mstart[i] + nElemA + nElemF] = \
F.data[F.data != 0][mstartF[i]:mstartF[i+1]]
mstart[i] = mstartA[i] + mstartF[i]
# The last value of mstart must be the total number of
# coefficients.
mstart[n_var] = mstartA[n_var] + mstartF[n_var]
# Q matrix is input via row/col indices and value, but only
# for the upper triangle. We just make it symmetric and twice
# itself, then, just remove all lower-triangular elements.
Q += Q.transpose()
Q /= 2
Q = Q.tocoo()
mqcol1 = Q.row[Q.row <= Q.col]
mqcol2 = Q.col[Q.row <= Q.col]
dqe = Q.data[Q.row <= Q.col]
colnames = ['x_{0:09d}'.format(i) for i in range(n_var)]
rownames = ['eq_{0:09d}'.format(i) for i in range(n_eq)] + \
['ineq_{0:09d}'.format(i) for i in range(n_ineq)]
if verbose:
self.prob_.controls.miplog = 2
self.prob_.controls.lplog = 1
self.prob_.controls.outputlog = 1
else:
self.prob_.controls.miplog = 0
self.prob_.controls.lplog = 0
self.prob_.controls.outputlog = 0
self.prob_.controls.xslp_log = -1
self.prob_.loadproblem(
probname='CVX_xpress_qp',
qrtypes=['E'] * n_eq + ['L'] * n_ineq,
rhs=list(b) + list(g),
range=None,
obj=q,
mstart=mstart,
mnel=None,
# linear coefficients
mrwind=mrwind,
dmatval=dmatval,
# variable bounds
dlb=[-xp.infinity] * n_var,
dub=[xp.infinity] * n_var,
# quadratic objective (only upper triangle)
mqcol1=mqcol1,
mqcol2=mqcol2,
dqe=dqe,
# binary and integer variables
qgtype=['B'] * len(data[s.BOOL_IDX]) +
['I'] * len(data[s.INT_IDX]),
mgcols=data[s.BOOL_IDX] + data[s.INT_IDX],
# variables' and constraints' names
colnames=colnames,
rownames=rownames)
# Set options
#
# The parameter solver_opts is a dictionary that contains only
# one key, 'solver_opt', and its value is a dictionary
# {'control': value}, matching perfectly the format used by
# the Xpress Python interface.
# Set options if compatible with Xpress problem control names
self.prob_.setControl({
i: solver_opts[i]
for i in solver_opts if hasattr(xp.controls.__dict__, i)
})
if 'bargaptarget' not in solver_opts.keys():
self.prob_.controls.bargaptarget = 1e-30
if 'feastol' not in solver_opts.keys():
self.prob_.controls.feastol = 1e-9
# Solve problem
results_dict = {"model": self.prob_}
try:
# If option given, write file before solving
if 'write_mps' in solver_opts.keys():
self.prob_.write(solver_opts['write_mps'])
self.prob_.solve()
results_dict["cputime"] = self.prob_.attributes.time
except xp.SolverError: # Error in the solution
results_dict["status"] = s.SOLVER_ERROR
else:
results_dict['status'] = self.prob_.getProbStatus()
results_dict[
'getProbStatusString'] = self.prob_.getProbStatusString()
results_dict['obj_value'] = self.prob_.getObjVal()
try:
results_dict[s.PRIMAL] = np.array(self.prob_.getSolution())
except xp.SolverError:
results_dict[s.PRIMAL] = np.zeros(self.prob_.attributes.ncol)
status_map_lp, status_map_mip = get_status_maps()
if results_dict['status'] == 'solver_error':
status = 'solver_error'
elif 'mip_' in results_dict['getProbStatusString']:
status = status_map_mip[results_dict['status']]
else:
status = status_map_lp[results_dict['status']]
results_dict['bariter'] = self.prob_.attributes.bariter
results_dict[
'getProbStatusString'] = self.prob_.getProbStatusString()
if status in s.SOLUTION_PRESENT:
results_dict['getObjVal'] = self.prob_.getObjVal()
results_dict['getSolution'] = self.prob_.getSolution()
if not (data[s.BOOL_IDX] or data[s.INT_IDX]):
results_dict['getDual'] = self.prob_.getDual()
del self.prob_
return results_dict
