# Source code for picos.modeling.solution

```
# ------------------------------------------------------------------------------
# Copyright (C) 2019 Maximilian Stahlberg
#
# This file is part of PICOS.
#
# PICOS is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# PICOS is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
# A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with
# this program. If not, see <http://www.gnu.org/licenses/>.
# ------------------------------------------------------------------------------
"""Optimization problem solution representation."""
import warnings
from .. import glyphs
from ..apidoc import api_end, api_start
_API_START = api_start(globals())
# -------------------------------
# Solution status strings, as verified by PICOS.
VS_UNKNOWN = "unverified"
"""PICOS failed to verify the solution."""
VS_DETACHED = "detached"
"""The solution is not attached to a problem (it was given by the user)."""
VS_EMPTY = "empty"
"""The solution is empty; there are neither primals nor duals."""
VS_DETACHED_EMPTY = "detached empty"
"""The solution is both detached and empty."""
VS_OUTDATED = "outdated"
"""The solution does not fit the problem formulation any more.
Variables or constraints were removed from the problem."""
VS_INCOMPLETE = "incomplete"
"""The primal (dual) solution does not concern all variables (constraints)."""
VS_FEASIBLE = "feasible"
"""The solution is primal feasible; there is no dual solution."""
VS_INFEASIBLE = "infeasible"
"""The solution is primal infeasible; there is no dual solution."""
VS_PRIMAL_FEASIBLE = "primal feasible"
"""The solution is primal feasible; a dual solution was not verified."""
VS_PRIMAL_INFEASIBLE = "primal infeasible"
"""The solution is primal infeasible; a dual solution was not verified."""
# Primal or dual solution (or search) status strings, as claimed by the solver.
SS_UNKNOWN = "unknown"
"""The solver did not make a clear claim about the solution status."""
SS_EMPTY = "empty"
"""The solver claims not to have produced a solution."""
SS_OPTIMAL = "optimal"
"""The solution is optimal."""
SS_FEASIBLE = "feasible"
"""The solution is feasible."""
SS_INFEASIBLE = "infeasible"
"""No feasible solution exists.
In the case of a primal solution, the problem is infeasible. In the case of a
dual solution, the problem is unbounded.
"""
SS_PREMATURE = "premature"
"""The search was prematurely terminated due to some limit."""
SS_FAILURE = "failure"
"""The search was termined due to a solver failure."""
# Problem status strings, as claimed by the solver.
PS_UNKNOWN = "unknown"
"""The solver did not make a clear claim about the problem status."""
PS_FEASIBLE = "feasible"
"""The problem is primal (and dual) feasible and bounded."""
PS_INFEASIBLE = "infeasible"
"""The problem is primal infeasible (and dual unbounded or infeasible)."""
PS_UNBOUNDED = "unbounded"
"""The problem is primal unbounded (and dual infeasible)."""
PS_INF_OR_UNB = "infeasible or unbounded"
"""The problem is primal infeasible or unbounded.
Being unbounded is usually infered from being dual infeasible."""
PS_UNSTABLE = "unstable"
"""The problem was found numerically unstable or otherwise hard to handle."""
PS_ILLPOSED = "illposed"
"""The problem was found to be in a state that is not amenable to solution."""
def _check_type(argument, *types):
"""Enforce the type of a method or function argument."""
for type_ in types:
if type_ is None:
type_ = type(None)
if isinstance(argument, type_):
return
raise TypeError("An argument is of type '{}' but must be instance of {}."
.format(type(argument).__name__, " or ".join("'{}'".format(t.__name__)
for t in types)))
# TODO: Make all public fields use snake_case, ensure backwards compatibility.
[docs]class Solution:
"""Assignment of primal and dual values to variables and constraints.
Instances are usually returned by a solver (and thus bound to a
:class:`problem <picos.Problem>` instance), but may be manually created by
the user:
>>> import picos
>>> P = picos.Problem()
>>> x = P.add_variable("x")
>>> s = picos.Solution({x: 1}); s
<detached primal solution from user>
>>> s.apply()
>>> x.value
1.0
If the solution was created by a solver (or attached to a problem via
:func:`attach_to`), more information is available:
>>> C1 = P.add_constraint(x >= 2)
>>> s = P.minimize(x, solver = "cvxopt", duals = False); s
<feasible primal solution (claimed optimal) from cvxopt>
>>> "{:.2f} ms".format(1000.0 * s.searchTime) #doctest: +SKIP
'0.83 ms'
>>> C2 = P.add_constraint(x >= 3); s
<infeasible primal solution (was feasible and claimed optimal) from cvxopt>
"""
[docs] def __init__(self, primals, duals=None, problem=None, solver="user",
primalStatus=SS_UNKNOWN, dualStatus=SS_UNKNOWN,
problemStatus=PS_UNKNOWN, searchTime=0.0, info=None,
vectorizedPrimals=False, reportedValue=None):
"""Create a solution to an optimization problem.
:param dict(picos.expressions.BaseVariable, object) primals:
A mapping of variables to their primal solution value.
:param dict(picos.constraints.Constraint, object) duals:
A mapping of constraints to their dual solution value.
:param picos.Problem problem:
The problem that was solved to create the solution. If ``None``,
then the solution is "detached".
:param str solver:
The name of the solver that was used to create the solution.
:param str primalStatus:
The primal solution status as reported by the solver.
:param str dualStatus:
The dual solution status as reported by the solver.
:param str problemStatus:
The state of the problem as reported by the solver.
:param float searchTime:
Seconds that the solution process took.
:param dict info:
Additional solution (meta)data.
:param bool vectorizedPrimals:
Whether primal solution values are given with respect to the
variable's special vectorization format as used by PICOS internally.
:param float reportedValue:
Objective value of the solution as reported by the solver.
"""
from ..expressions import BaseVariable
from ..constraints import Constraint
from .problem import Problem
if primals is None:
primals = {}
if duals is None:
duals = {}
if info is None:
info = {}
# Be strict about the arguments as they are handed to the user.
_check_type(primals, dict)
_check_type(duals, dict)
_check_type(problem, None, Problem)
_check_type(solver, str)
_check_type(primalStatus, str)
_check_type(dualStatus, str)
_check_type(problemStatus, str)
_check_type(searchTime, float)
_check_type(info, dict)
_check_type(vectorizedPrimals, bool)
_check_type(reportedValue, None, float)
for variable, _ in primals.items():
if not isinstance(variable, BaseVariable):
raise TypeError("They keys in the primals argument of "
"Solution.__init__ must be variables.")
for constraint, _ in duals.items():
if not isinstance(constraint, Constraint):
raise TypeError("They keys in the duals argument of "
"Solution.__init__ must be constraints.")
# Derive a "claimed status" from the claimed primal and dual states.
if primals and duals:
if primalStatus == dualStatus:
claimedStatus = primalStatus
else:
claimedStatus = "primal {} and dual {}".format(
primalStatus, dualStatus)
elif primals:
# Do not warn about correctingdualStatus, because the solver might
# have produced primals but PICOS did not read them.
dualStatus = SS_EMPTY
claimedStatus = primalStatus
elif duals:
# Do not warn about correcting primalStatus, because the solver
# might have produced duals but PICOS did not read them.
primalStatus = SS_EMPTY
claimedStatus = dualStatus
else:
primalStatus = SS_EMPTY
dualStatus = SS_EMPTY
claimedStatus = SS_EMPTY
# Infeasible problem implies infeasible primal.
if problemStatus == PS_INFEASIBLE \
and primalStatus not in (SS_INFEASIBLE, SS_EMPTY):
warnings.warn(
"{} claims that a problem is infeasible but does not say the "
"same about the nonempty primal solution. Correcting this.".
format(solver), RuntimeWarning)
primalStatus = SS_INFEASIBLE
# Unbounded problem implies infeasible dual.
if problemStatus == PS_UNBOUNDED \
and dualStatus not in (SS_INFEASIBLE, SS_EMPTY):
warnings.warn(
"{} claims that a problem is unbounded but does not say that "
"the nonempty dual solution is infeasible. Correcting this.".
format(solver), RuntimeWarning)
dualStatus = SS_INFEASIBLE
# Optimal solution implies feasible problem.
if claimedStatus == SS_OPTIMAL and problemStatus != PS_FEASIBLE:
warnings.warn(
"{} claims to have found an optimal solution but does not say "
" that the problem is feasible. Correcting this."
.format(solver), RuntimeWarning)
problemStatus = PS_FEASIBLE
self.problem = problem
"""The problem that was solved to produce the solution."""
self.solver = solver
"""The solver that produced the solution."""
self.searchTime = searchTime
"""Time in seconds that the solution search took."""
self.primals = primals
"""The primal solution values returned by the solver."""
self.duals = duals
"""The dual solution values returned by the solver."""
self.info = info
"""Additional information provided by the solver."""
self.lastStatus = VS_UNKNOWN
"""The solution status as verified by PICOS when the solution was
applied to the problem."""
self.primalStatus = primalStatus
"""The primal solution status as claimed by the solver."""
self.dualStatus = dualStatus
"""The dual solution status as claimed by the solver."""
self.claimedStatus = claimedStatus
"""The primal and dual solution status as claimed by the solver."""
self.problemStatus = problemStatus
"""The problem status as claimed by the solver."""
self.vectorizedPrimals = vectorizedPrimals
"""Whether primal values refer to variables' special vectorizations."""
self.reportedValue = reportedValue
"""The objective value of the solution as reported by the solver."""
def _status_of_problem(self, problem):
"""Retrieve the problem's verified solution status.
Requires that the solution has just been applied to the problem.
"""
if not self.primals and not self.duals:
return VS_EMPTY
try:
isFeasible = problem.check_current_value_feasibility()[0]
except LookupError:
return VS_INCOMPLETE
except Exception:
return VS_UNKNOWN
if isFeasible:
return VS_PRIMAL_FEASIBLE if self.duals else VS_FEASIBLE
else:
return VS_PRIMAL_INFEASIBLE if self.duals else VS_INFEASIBLE
@property
def status(self):
"""The current solution status as verified by PICOS.
.. warning::
Accessing this attribute is expensive for large problems as a copy
of the problem needs to be created and valued. If you have just
applied the solution to a :class:`problem <picos.Problem>`, query
the solution's lastStatus attribute instead.
"""
if not self.primals and not self.duals:
if not self.problem:
return VS_DETACHED_EMPTY
else:
return VS_EMPTY
elif not self.problem:
return VS_DETACHED
elif not self.primals:
return VS_UNKNOWN
problemCopy = self.problem.copy()
try:
self.apply(toProblem=problemCopy)
except RuntimeError:
return VS_OUTDATED
return self._status_of_problem(problemCopy)
@property
def value(self):
"""The objective value of the solution as computed by PICOS.
.. warning::
Accessing this attribute is expensive for large problems as a copy
of the problem needs to be created and valued. If you have just
applied the solution to a :class:`problem <picos.Problem>`, query
that problem instead.
"""
if not self.problem:
raise RuntimeError(
"Cannot compute the objective value of a detached solution. "
"Use attach_to to assign the solution to a problem.")
problemCopy = self.problem.copy()
self.apply(toProblem=problemCopy)
return problemCopy.value
@property
def reported_value(self):
"""The objective value as reported by the solver, or :obj:`None`."""
return self.reportedValue
def __str__(self):
verifiedStatus = self.status
lastStatus = self.lastStatus
claimedStatus = self.claimedStatus
problemStatus = self.problemStatus
if self.primals and self.duals:
solutionType = "solution pair"
elif self.primals:
solutionType = "primal solution"
elif self.duals:
solutionType = "dual solution"
else:
solutionType = "solution" # "(detached) empty solution"
# Print the last status if it is known and differs from the current one.
printLastStatus = lastStatus != VS_UNKNOWN and \
verifiedStatus != lastStatus
# Print the claimed status only if it differs from the initial verified
# one is not implied by a problem status that will be printed.
printClaimedStatus = \
claimedStatus not in (verifiedStatus, SS_UNKNOWN) and \
problemStatus not in (PS_INFEASIBLE, PS_UNBOUNDED)
# Print the problem status only if it is interesting.
printProblemStatus = \
problemStatus not in (PS_UNKNOWN, PS_FEASIBLE)
if printLastStatus and printClaimedStatus:
unverifiedStatus = " (was {} and claimed {})".format(
lastStatus, claimedStatus)
elif printLastStatus:
unverifiedStatus = " (was {})".format(lastStatus)
elif printClaimedStatus:
unverifiedStatus = " (claimed {})".format(claimedStatus)
else:
unverifiedStatus = ""
if printProblemStatus:
unverifiedStatus += \
" for a problem claimed {}".format(problemStatus)
return "{} {}{} from {}".format(verifiedStatus, solutionType,
unverifiedStatus, self.solver)
def __repr__(self):
return glyphs.repr1(self.__str__())
[docs] def apply(self, primals=True, duals=True, clearOnNone=True, toProblem=None,
snapshotStatus=False):
"""Apply the solution to the involved variables and constraints.
:param bool primals: Whether to apply the primal solution.
:param bool duals: Whether to apply the dual solution.
:param bool clearOnNone: Whether to clear the value of a variable or
constraint if the solution has it set to None. This could happen in
case of an error or shortcoming of the solver or PICOS.
:param picos.Problem toProblem: If set to a copy of the problem that was
used to produce the solution, will apply the solution to that copy's
variables and constraints instead.
:param bool snapshotStatus: Whether to update the lastStatus attribute
with the new (verified) solution status. PICOS enables this whenever
it applies a solution returned by a solver.
"""
if toProblem:
if primals:
thePrimals = {}
try:
for variable, primal in self.primals.items():
thePrimals[toProblem.variables[variable.name]] = primal
except KeyError as error:
raise RuntimeError(
"Cannot apply solution to specified problem as not all "
"variables for which primal values exist were found.") \
from error
if duals:
theDuals = {}
try:
for constraint, dual in self.duals.items():
theDuals[toProblem.constraints[constraint.id]] = dual
except KeyError as error:
raise RuntimeError(
"Cannot apply solution to specified problem as not all "
"constraints for which dual values exist were found.") \
from error
else:
thePrimals = self.primals
theDuals = self.duals
if primals:
for variable, primal in thePrimals.items():
if primal is None and not clearOnNone:
continue
if self.vectorizedPrimals:
variable.internal_value = primal
else:
variable.value = primal
if duals:
for constraint, dual in theDuals.items():
if dual is None and not clearOnNone:
continue
constraint.dual = dual
if snapshotStatus:
if toProblem:
self.lastStatus = self._status_of_problem(toProblem)
elif self.problem:
self.lastStatus = self._status_of_problem(self.problem)
else: # detached solution
self.lastStatus = self.status
if toProblem:
toProblem._last_solution = self
elif self.problem:
self.problem._last_solution = self
[docs] def attach_to(self, problem, snapshotStatus=False):
"""Attach (or move) the solution to a problem.
Only variables and constraints that exist on the problem (same name or
ID, respectively) are kept.
:param bool snapshotStatus: Whether to set the lastStatus attribute
of the copy to match the new problem.
"""
self.problem = problem
# Find variables of same name in the problem and assign primals.
oldPrimals, self.primals = self.primals, {}
for variable, primal in oldPrimals.items():
if variable.name in problem.variables:
self.primals[problem.variables[variable.name]] = primal
# Find constraints of same ID in the problem and assign duals.
oldDuals, self.duals = self.duals, {}
for constraint, dual in oldDuals.items():
if constraint.id in problem.constraints:
self.duals[problem.constraints[constraint.id]] = dual
# Update the last (verified) status.
if snapshotStatus:
self.lastStatus = problem.status
else:
self.lastStatus = VS_UNKNOWN
# --------------------------------------
__all__ = api_end(_API_START, globals())
```