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add cuopt direct solver
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‎pyomo/solvers/plugins/solvers/__init__.py‎

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gurobi_persistent,
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cplex_direct,
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cplex_persistent,
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cuopt_direct,
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GAMS,
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mosek_direct,
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mosek_persistent,

‎pyomo/solvers/plugins/solvers/cuopt_direct.py‎

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# ___________________________________________________________________________
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#
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# Pyomo: Python Optimization Modeling Objects
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# Copyright (c) 2008-2025
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# National Technology and Engineering Solutions of Sandia, LLC
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# Under the terms of Contract DE-NA0003525 with National Technology and
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# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
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# rights in this software.
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# This software is distributed under the 3-clause BSD License.
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# ___________________________________________________________________________
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import logging
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import re
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import sys
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from pyomo.common.collections import ComponentSet, ComponentMap, Bunch
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from pyomo.common.dependencies import attempt_import
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from pyomo.core.base import Suffix, Var, Constraint, SOSConstraint, Objective
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from pyomo.common.errors import ApplicationError
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from pyomo.common.tempfiles import TempfileManager
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from pyomo.common.tee import capture_output
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from pyomo.core.expr.numvalue import is_fixed
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from pyomo.core.expr.numvalue import value
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from pyomo.core.staleflag import StaleFlagManager
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from pyomo.repn import generate_standard_repn
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from pyomo.solvers.plugins.solvers.direct_solver import DirectSolver
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from pyomo.solvers.plugins.solvers.direct_or_persistent_solver import (
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DirectOrPersistentSolver,
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)
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from pyomo.core.kernel.objective import minimize, maximize
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from pyomo.opt.results.results_ import SolverResults
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from pyomo.opt.results.solution import Solution, SolutionStatus
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from pyomo.opt.results.solver import TerminationCondition, SolverStatus
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from pyomo.opt.base import SolverFactory
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from pyomo.core.base.suffix import Suffix
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import numpy as np
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import time
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logger = logging.getLogger('pyomo.solvers')
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cuopt, cuopt_available = attempt_import(
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'cuopt',
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)
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@SolverFactory.register('cuopt_direct', doc='Direct python interface to CUOPT')
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class CUOPTDirect(DirectSolver):
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def __init__(self, **kwds):
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kwds['type'] = 'cuoptdirect'
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super(CUOPTDirect, self).__init__(**kwds)
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self._python_api_exists = True
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def _apply_solver(self):
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StaleFlagManager.mark_all_as_stale()
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log_file = None
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if self._log_file:
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log_file = self._log_file
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t0 = time.time()
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self.solution = cuopt.linear_programming.solver.Solve(self._solver_model)
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t1 = time.time()
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self._wallclock_time = t1 - t0
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return Bunch(rc=None, log=None)
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def _add_constraint(self, constraints):
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c_lb, c_ub = [], []
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matrix_data, matrix_indptr, matrix_indices = [], [0], []
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for i, con in enumerate(constraints):
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repn = generate_standard_repn(con.body, quadratic=False)
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matrix_data.extend(repn.linear_coefs)
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matrix_indices.extend([self.var_name_dict[str(i)] for i in repn.linear_vars])
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"""for v, c in zip(con.body.linear_vars, con.body.linear_coefs):
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matrix_data.append(value(c))
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matrix_indices.append(self.var_name_dict[str(v)])"""
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matrix_indptr.append(len(matrix_data))
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c_lb.append(value(con.lower) if con.lower is not None else -np.inf)
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c_ub.append(value(con.upper) if con.upper is not None else np.inf)
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self._solver_model.set_csr_constraint_matrix(np.array(matrix_data), np.array(matrix_indices), np.array(matrix_indptr))
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self._solver_model.set_constraint_lower_bounds(np.array(c_lb))
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self._solver_model.set_constraint_upper_bounds(np.array(c_ub))
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def _add_var(self, variables):
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# Map vriable to index and get var bounds
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var_type_dict = {"Integers": 'I', "Reals": 'C', "Binary": 'I'} # NonNegativeReals ?
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self.var_name_dict = {}
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v_lb, v_ub, v_type = [], [], []
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for i, v in enumerate(variables):
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v_type.append(var_type_dict[str(v.domain)])
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if v.domain == "Binary":
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v_lb.append(0)
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v_ub.append(1)
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else:
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v_lb.append(v.lb if v.lb is not None else -np.inf)
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v_ub.append(v.ub if v.ub is not None else np.inf)
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self.var_name_dict[str(v)] = i
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self._pyomo_var_to_ndx_map[v] = self._ndx_count
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self._ndx_count += 1
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self._solver_model.set_variable_lower_bounds(np.array(v_lb))
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self._solver_model.set_variable_upper_bounds(np.array(v_ub))
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self._solver_model.set_variable_types(np.array(v_type))
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self._solver_model.set_variable_names(np.array(list(self.var_name_dict.keys())))
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def _set_objective(self, objective):
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repn = generate_standard_repn(objective.expr, quadratic=False)
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obj_coeffs = [0] * len(self.var_name_dict)
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for i, coeff in enumerate(repn.linear_coefs):
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obj_coeffs[self.var_name_dict[str(repn.linear_vars[i])]] = coeff
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self._solver_model.set_objective_coefficients(np.array(obj_coeffs))
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if objective.sense == maximize:
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self._solver_model.set_maximize(True)
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def _set_instance(self, model, kwds={}):
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DirectOrPersistentSolver._set_instance(self, model, kwds)
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self.var_name_dict = None
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self._pyomo_var_to_ndx_map = ComponentMap()
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self._ndx_count = 0
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try:
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self._solver_model = cuopt.linear_programming.DataModel()
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except Exception:
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e = sys.exc_info()[1]
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msg = (
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"Unable to create CUOPT model. "
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"Have you installed the Python "
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"SDK for CUOPT?\n\n\t" + "Error message: {0}".format(e)
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)
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self._add_block(model)
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def _add_block(self, block):
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self._add_var(block.component_data_objects(
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ctype=Var, descend_into=True, active=True, sort=True)
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)
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for sub_block in block.block_data_objects(descend_into=True, active=True):
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self._add_constraint(sub_block.component_data_objects(
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ctype=Constraint, descend_into=False, active=True, sort=True)
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)
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obj_counter = 0
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for obj in sub_block.component_data_objects(
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ctype=Objective, descend_into=False, active=True
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):
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obj_counter += 1
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if obj_counter > 1:
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raise ValueError(
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"Solver interface does not support multiple objectives."
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)
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self._set_objective(obj)
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def _postsolve(self):
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extract_duals = False
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extract_slacks = False
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extract_reduced_costs = False
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for suffix in self._suffixes:
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flag = False
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if re.match(suffix, "rc"):
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extract_reduced_costs = True
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flag = True
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if not flag:
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raise RuntimeError(
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"***The cuopt_direct solver plugin cannot extract solution suffix="
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+ suffix
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)
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solution = self.solution
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status = solution.get_termination_status()
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self.results = SolverResults()
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soln = Solution()
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self.results.solver.name = "CUOPT"
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self.results.solver.wallclock_time = self._wallclock_time
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prob_type = solution.problem_category
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if status in [1]:
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self.results.solver.status = SolverStatus.ok
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self.results.solver.termination_condition = TerminationCondition.optimal
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soln.status = SolutionStatus.optimal
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elif status in [3]:
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self.results.solver.status = SolverStatus.warning
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self.results.solver.termination_condition = TerminationCondition.unbounded
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soln.status = SolutionStatus.unbounded
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elif status in [8]:
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self.results.solver.status = SolverStatus.ok
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self.results.solver.termination_condition = TerminationCondition.feasible
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soln.status = SolutionStatus.feasible
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elif status in [2]:
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self.results.solver.status = SolverStatus.warning
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self.results.solver.termination_condition = TerminationCondition.infeasible
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soln.status = SolutionStatus.infeasible
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elif status in [4]:
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self.results.solver.status = SolverStatus.aborted
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self.results.solver.termination_condition = (
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TerminationCondition.maxIterations
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)
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soln.status = SolutionStatus.stoppedByLimit
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elif status in [5]:
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self.results.solver.status = SolverStatus.aborted
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self.results.solver.termination_condition = (
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TerminationCondition.maxTimeLimit
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)
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soln.status = SolutionStatus.stoppedByLimit
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elif status in [7]:
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self.results.solver.status = SolverStatus.ok
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self.results.solver.termination_condition = (
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TerminationCondition.other
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)
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soln.status = SolutionStatus.other
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else:
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self.results.solver.status = SolverStatus.error
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self.results.solver.termination_condition = TerminationCondition.error
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soln.status = SolutionStatus.error
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if self._solver_model.maximize:
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self.results.problem.sense = maximize
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else:
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self.results.problem.sense = minimize
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self.results.problem.upper_bound = None
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self.results.problem.lower_bound = None
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try:
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self.results.problem.upper_bound = solution.get_primal_objective()
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self.results.problem.lower_bound = solution.get_primal_objective()
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except Exception as e:
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pass
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var_map = self._pyomo_var_to_ndx_map
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primal_solution = solution.get_primal_solution().tolist()
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for i, pyomo_var in enumerate(var_map.keys()):
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pyomo_var.set_value(primal_solution[i], skip_validation=True)
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if extract_reduced_costs:
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self._load_rc()
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self.results.solution.insert(soln)
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return DirectOrPersistentSolver._postsolve(self)
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def warm_start_capable(self):
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return False
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def _load_rc(self, vars_to_load=None):
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if not hasattr(self._pyomo_model, 'rc'):
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self._pyomo_model.rc = Suffix(direction=Suffix.IMPORT)
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rc = self._pyomo_model.rc
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var_map = self._pyomo_var_to_ndx_map
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if vars_to_load is None:
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vars_to_load = var_map.keys()
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reduced_costs = self.solution.get_reduced_costs()
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for pyomo_var in vars_to_load:
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rc[pyomo_var] = reduced_costs[var_map[pyomo_var]]
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def load_rc(self, vars_to_load):
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"""
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Load the reduced costs into the 'rc' suffix. The 'rc' suffix must live on the parent model.
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Parameters
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----------
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vars_to_load: list of Var
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"""
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self._load_rc(vars_to_load)

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