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- Symengine-based symbolics is now a fully supported feature - Added the first exact LP solver, available as glpk_exact_interface - Fixed an issue with indicator variables in cplex - Minor bugfixes
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| Original file line number | Diff line number | Diff line change |
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| import numpy as np | ||
| from optlang import Model, Variable, Constraint, Objective | ||
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| # All the (symbolic) variables are declared, with a name and optionally a lower | ||
| # and/or upper bound. | ||
| x = np.array([Variable('x{}'.format(i), lb=0) for i in range(1, 4)]) | ||
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| bounds = [100, 600, 300] | ||
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| A = np.array([[1, 1, 1], | ||
| [10, 4, 5], | ||
| [2, 2, 6]]) | ||
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| w = np.array([10, 6, 4]) | ||
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| obj = Objective(w.dot(x), direction='max') | ||
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| c = np.array([Constraint(row, ub=bound) for row, bound in zip(A.dot(x), bounds)]) | ||
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| model = Model(name='Numpy model') | ||
| model.objective = obj | ||
| model.add(c) | ||
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| status = model.optimize() | ||
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| print("status:", model.status) | ||
| print("objective value:", model.objective.value) | ||
| print("----------") | ||
| for var_name, var in model.variables.iteritems(): | ||
| print(var_name, "=", var.primal) |
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| # Copyright 2017 Novo Nordisk Foundation Center for Biosustainability, | ||
| # Technical University of Denmark. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| """ | ||
| Interface for the GNU Linear Programming Kit (GLPK) | ||
| GLPK is an open source LP solver, with MILP capabilities. This interface exposes its GLPK's exact solver. | ||
| To use GLPK you need to install the 'swiglpk' python package (with pip or from http://github.com/biosustain/swiglpk) | ||
| and make sure that 'import swiglpk' runs without error. | ||
| """ | ||
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| import logging | ||
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| import six | ||
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| from optlang.util import inheritdocstring | ||
| from optlang import interface | ||
| from optlang import glpk_interface | ||
| from optlang.glpk_interface import _GLPK_STATUS_TO_STATUS | ||
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| log = logging.getLogger(__name__) | ||
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| from swiglpk import glp_exact, glp_create_prob, glp_get_status, \ | ||
| GLP_SF_AUTO, GLP_ETMLIM, glp_adv_basis, glp_read_lp, glp_scale_prob | ||
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| @six.add_metaclass(inheritdocstring) | ||
| class Variable(glpk_interface.Variable): | ||
| def __init__(self, name, index=None, type="continuous", **kwargs): | ||
| if type in ("integer", "binary"): | ||
| raise ValueError("The GLPK exact solver does not support integer and mixed integer problems") | ||
| super(Variable, self).__init__(name, index, type=type, **kwargs) | ||
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| @glpk_interface.Variable.type.setter | ||
| def type(self, value): | ||
| if value in ("integer", "binary"): | ||
| raise ValueError("The GLPK exact solver does not support integer and mixed integer problems") | ||
| super(Variable, Variable).type.fset(self, value) | ||
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| @six.add_metaclass(inheritdocstring) | ||
| class Constraint(glpk_interface.Constraint): | ||
| pass | ||
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| @six.add_metaclass(inheritdocstring) | ||
| class Objective(glpk_interface.Objective): | ||
| pass | ||
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| @six.add_metaclass(inheritdocstring) | ||
| class Configuration(glpk_interface.Configuration): | ||
| pass | ||
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| @six.add_metaclass(inheritdocstring) | ||
| class Model(glpk_interface.Model): | ||
| def _run_glp_exact(self): | ||
| return_value = glp_exact(self.problem, self.configuration._smcp) | ||
| glpk_status = glp_get_status(self.problem) | ||
| if return_value == 0: | ||
| status = _GLPK_STATUS_TO_STATUS[glpk_status] | ||
| elif return_value == GLP_ETMLIM: | ||
| status = interface.TIME_LIMIT | ||
| else: | ||
| status = _GLPK_STATUS_TO_STATUS[glpk_status] | ||
| if status == interface.UNDEFINED: | ||
| log.debug("Status undefined. GLPK status code returned by glp_simplex was %d" % return_value) | ||
| return status | ||
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| def _optimize(self): | ||
| # Solving inexact first per GLPK manual | ||
| # Computations in exact arithmetic are very time consuming, so solving LP | ||
| # problem with the routine glp_exact from the very beginning is not a good | ||
| # idea. It is much better at first to find an optimal basis with the routine | ||
| # glp_simplex and only then to call glp_exact, in which case only a few | ||
| # simplex iterations need to be performed in exact arithmetic. | ||
| status = super(Model, self)._optimize() | ||
| if status != interface.OPTIMAL: | ||
| return status | ||
| else: | ||
| status = self._run_glp_exact() | ||
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| if status == interface.UNDEFINED and self.configuration.presolve is True: | ||
| # If presolve is on, status will be undefined if not optimal | ||
| self.configuration.presolve = False | ||
| status = self._run_glp_exact() | ||
| self.configuration.presolve = True | ||
| return status | ||
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| if __name__ == '__main__': | ||
| import pickle | ||
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| x1 = Variable('x1', lb=0) | ||
| x2 = Variable('x2', lb=0) | ||
| x3 = Variable('x3', lb=0, ub=1, type='binary') | ||
| c1 = Constraint(x1 + x2 + x3, lb=-100, ub=100, name='c1') | ||
| c2 = Constraint(10 * x1 + 4 * x2 + 5 * x3, ub=600, name='c2') | ||
| c3 = Constraint(2 * x1 + 2 * x2 + 6 * x3, ub=300, name='c3') | ||
| obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max') | ||
| model = Model(name='Simple model') | ||
| model.objective = obj | ||
| model.add([c1, c2, c3]) | ||
| model.configuration.verbosity = 3 | ||
| status = model.optimize() | ||
| print("status:", model.status) | ||
| print("objective value:", model.objective.value) | ||
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| for var_name, var in model.variables.items(): | ||
| print(var_name, "=", var.primal) | ||
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| print(model) | ||
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| problem = glp_create_prob() | ||
| glp_read_lp(problem, None, "tests/data/model.lp") | ||
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| solver = Model(problem=problem) | ||
| print(solver.optimize()) | ||
| print(solver.objective) | ||
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| import time | ||
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| t1 = time.time() | ||
| print("pickling") | ||
| pickle_string = pickle.dumps(solver) | ||
| resurrected_solver = pickle.loads(pickle_string) | ||
| t2 = time.time() | ||
| print("Execution time: %s" % (t2 - t1)) | ||
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| resurrected_solver.optimize() | ||
| print("Halelujah!", resurrected_solver.objective.value) |
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