Skip to content

Commit

Permalink
Merge pull request #2921 from jsiirola/xpress-nlp-status-fix
Browse files Browse the repository at this point in the history 
Xpress: fix NLP results processing for Xpress 9.2.0
  • Loading branch information
@jsiirola
jsiirola authored Jul 25, 2023
2 parents 228f5eb + fcac472 commit 3379349
Showing 1 changed file with 82 additions and 82 deletions.
164 changes: 82 additions & 82 deletions pyomo/solvers/plugins/solvers/xpress_direct.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -436,98 +436,98 @@ def _get_nlp_results(self, results, soln):
# was convex
if (xprob_attrs.originalmipents > 0) or (xprob_attrs.originalsets > 0):
return self._get_mip_results(results, soln)
else:
elif xprob_attrs.lpstatus and not xprob_attrs.xslp_nlpstatus:
# If there is no NLP solver status, process the result
# using the LP results processor.
return self._get_lp_results(results, soln)
else:
# The problem was non-linear
status = xprob_attrs.xslp_nlpstatus
solstatus = xprob_attrs.xslp_solstatus
have_soln = False
optimal = False # *globally* optimal?
if status == xp.nlp_unstarted:
results.solver.status = SolverStatus.unknown
results.solver.termination_message = (
"Non-convex model solve was not start"
)
results.solver.termination_condition = TerminationCondition.unknown
soln.status = SolutionStatus.unknown
elif status == xp.nlp_locally_optimal:
# This is either xp.nlp_locally_optimal or xp.nlp_solution
# we must look at the solstatus to figure out which
if solstatus in [2, 3]:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Non-convex model was solved to local optimality"
)
results.solver.termination_condition = (
TerminationCondition.locallyOptimal
)
soln.status = SolutionStatus.locallyOptimal
else:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Feasible solution found for non-convex model"
)
results.solver.termination_condition = TerminationCondition.feasible
soln.status = SolutionStatus.feasible
have_soln = True
elif status == xp.nlp_globally_optimal:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Non-convex model was solved to global optimality"
)
results.solver.termination_condition = TerminationCondition.optimal
soln.status = SolutionStatus.optimal
have_soln = True
optimal = True
elif status == xp.nlp_locally_infeasible:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Non-convex model was proven to be locally infeasible"
)
results.solver.termination_condition = TerminationCondition.noSolution
soln.status = SolutionStatus.unknown
elif status == xp.nlp_infeasible:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Non-conex model was proven to be infeasible"
)
results.solver.termination_condition = TerminationCondition.infeasible
soln.status = SolutionStatus.infeasible
elif status == xp.nlp_unbounded: # locally unbounded!

# The problem was non-linear
status = xprob_attrs.xslp_nlpstatus
solstatus = xprob_attrs.xslp_solstatus
have_soln = False
optimal = False # *globally* optimal?
if status == xp.nlp_unstarted:
results.solver.status = SolverStatus.unknown
results.solver.termination_message = (
"Non-convex model solve was not started"
)
results.solver.termination_condition = TerminationCondition.unknown
soln.status = SolutionStatus.unknown
elif status == xp.nlp_locally_optimal:
# This is either xp.nlp_locally_optimal or xp.nlp_solution
# we must look at the solstatus to figure out which
if solstatus in [2, 3]:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Non-convex model is locally unbounded"
"Non-convex model was solved to local optimality"
)
results.solver.termination_condition = TerminationCondition.unbounded
soln.status = SolutionStatus.unbounded
elif status == xp.nlp_unfinished:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Non-convex solve not finished (numerical issues?)"
results.solver.termination_condition = (
TerminationCondition.locallyOptimal
)
results.solver.termination_condition = TerminationCondition.unknown
soln.status = SolutionStatus.unknown
have_soln = True
soln.status = SolutionStatus.locallyOptimal
else:
results.solver.status = SolverStatus.error
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Error for non-convex model: " + str(status)
"Feasible solution found for non-convex model"
)
results.solver.termination_condition = TerminationCondition.error
soln.status = SolutionStatus.error
results.solver.termination_condition = TerminationCondition.feasible
soln.status = SolutionStatus.feasible
have_soln = True
elif status == xp.nlp_globally_optimal:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Non-convex model was solved to global optimality"
)
results.solver.termination_condition = TerminationCondition.optimal
soln.status = SolutionStatus.optimal
have_soln = True
optimal = True
elif status == xp.nlp_locally_infeasible:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Non-convex model was proven to be locally infeasible"
)
results.solver.termination_condition = TerminationCondition.noSolution
soln.status = SolutionStatus.unknown
elif status == xp.nlp_infeasible:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Non-convex model was proven to be infeasible"
)
results.solver.termination_condition = TerminationCondition.infeasible
soln.status = SolutionStatus.infeasible
elif status == xp.nlp_unbounded: # locally unbounded!
results.solver.status = SolverStatus.ok
results.solver.termination_message = "Non-convex model is locally unbounded"
results.solver.termination_condition = TerminationCondition.unbounded
soln.status = SolutionStatus.unbounded
elif status == xp.nlp_unfinished:
results.solver.status = SolverStatus.ok
results.solver.termination_message = (
"Non-convex solve not finished (numerical issues?)"
)
results.solver.termination_condition = TerminationCondition.unknown
soln.status = SolutionStatus.unknown
have_soln = True
else:
results.solver.status = SolverStatus.error
results.solver.termination_message = "Error for non-convex model: " + str(
status
)
results.solver.termination_condition = TerminationCondition.error
soln.status = SolutionStatus.error

results.problem.upper_bound = None
results.problem.lower_bound = None
try:
if xprob_attrs.objsense > 0.0 or optimal: # minimizing
results.problem.upper_bound = xprob_attrs.xslp_objval
if xprob_attrs.objsense < 0.0 or optimal: # maximizing
results.problem.lower_bound = xprob_attrs.xslp_objval
except (XpressDirect.XpressException, AttributeError):
pass
results.problem.upper_bound = None
results.problem.lower_bound = None
try:
if xprob_attrs.objsense > 0.0 or optimal: # minimizing
results.problem.upper_bound = xprob_attrs.xslp_objval
if xprob_attrs.objsense < 0.0 or optimal: # maximizing
results.problem.lower_bound = xprob_attrs.xslp_objval
except (XpressDirect.XpressException, AttributeError):
pass

return have_soln
return have_soln

def _solve_model(self):
xprob = self._solver_model
Expand Down

0 comments on commit 3379349

Please sign in to comment.