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generic_backend.pyx
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generic_backend.pyx
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r"""
Generic Backend for LP solvers
This class only lists the methods that should be defined by any
interface with a LP Solver. All these methods immediately raise
``NotImplementedError`` exceptions when called, and are obviously
meant to be replaced by the solver-specific method. This file can also
be used as a template to create a new interface : one would only need
to replace the occurrences of ``"Nonexistent_LP_solver"`` by the
solver's name, and replace ``GenericBackend`` by
``SolverName(GenericBackend)`` so that the new solver extends this
class.
AUTHORS:
- Nathann Cohen (2010-10) : initial implementation
- Risan (2012-02) : extension for PPL backend
- Ingolfur Edvardsson (2014-06): extension for CVXOPT backend
"""
#*****************************************************************************
# Copyright (C) 2010 Nathann Cohen <nathann.cohen@gmail.com>
#
# This program 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 2 of the License, or
# (at your option) any later version.
# http://www.gnu.org/licenses/
#*****************************************************************************
from __future__ import print_function
from copy import copy
cdef class GenericBackend:
cpdef base_ring(self):
from sage.rings.all import RDF
return RDF
cpdef zero(self):
return self.base_ring()(0)
cpdef int add_variable(self, lower_bound=0, upper_bound=None,
binary=False, continuous=True, integer=False,
obj=None, name=None) except -1:
"""
Add a variable.
This amounts to adding a new column to the matrix. By default,
the variable is both positive and real.
INPUT:
- ``lower_bound`` - the lower bound of the variable (default: 0)
- ``upper_bound`` - the upper bound of the variable (default: ``None``)
- ``binary`` - ``True`` if the variable is binary (default: ``False``).
- ``continuous`` - ``True`` if the variable is binary (default: ``True``).
- ``integer`` - ``True`` if the variable is binary (default: ``False``).
- ``obj`` - (optional) coefficient of this variable in the objective function (default: 0.0)
- ``name`` - an optional name for the newly added variable (default: ``None``).
OUTPUT: The index of the newly created variable
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.ncols() # optional - Nonexistent_LP_solver
0
sage: p.add_variable() # optional - Nonexistent_LP_solver
0
sage: p.ncols() # optional - Nonexistent_LP_solver
1
sage: p.add_variable(binary=True) # optional - Nonexistent_LP_solver
1
sage: p.add_variable(lower_bound=-2.0, integer=True) # optional - Nonexistent_LP_solver
2
sage: p.add_variable(continuous=True, integer=True) # optional - Nonexistent_LP_solver
Traceback (most recent call last):
...
ValueError: ...
sage: p.add_variable(name='x',obj=1.0) # optional - Nonexistent_LP_solver
3
sage: p.col_name(3) # optional - Nonexistent_LP_solver
'x'
sage: p.objective_coefficient(3) # optional - Nonexistent_LP_solver
1.0
"""
raise NotImplementedError()
cpdef int add_variables(self, int n, lower_bound=False, upper_bound=None, binary=False, continuous=True, integer=False, obj=None, names=None) except -1:
"""
Add ``n`` variables.
This amounts to adding new columns to the matrix. By default,
the variables are both nonnegative and real.
INPUT:
- ``n`` - the number of new variables (must be > 0)
- ``lower_bound`` - the lower bound of the variable (default: 0)
- ``upper_bound`` - the upper bound of the variable (default: ``None``)
- ``binary`` - ``True`` if the variable is binary (default: ``False``).
- ``continuous`` - ``True`` if the variable is binary (default: ``True``).
- ``integer`` - ``True`` if the variable is binary (default: ``False``).
- ``obj`` - (optional) coefficient of all variables in the objective function (default: 0.0)
- ``names`` - optional list of names (default: ``None``)
OUTPUT: The index of the variable created last.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.ncols() # optional - Nonexistent_LP_solver
0
sage: p.add_variables(5) # optional - Nonexistent_LP_solver
4
sage: p.ncols() # optional - Nonexistent_LP_solver
5
sage: p.add_variables(2, lower_bound=-2.0, integer=True, names=['a','b']) # optional - Nonexistent_LP_solver
6
TESTS:
Check that arguments are used::
sage: p.col_bounds(5) # tol 1e-8, optional - Nonexistent_LP_solver
(-2.0, None)
sage: p.is_variable_integer(5) # optional - Nonexistent_LP_solver
True
sage: p.col_name(5) # optional - Nonexistent_LP_solver
'a'
sage: p.objective_coefficient(5) # tol 1e-8, optional - Nonexistent_LP_solver
42.0
"""
cdef int i
cdef int value
if lower_bound is False:
lower_bound = self.zero()
if obj is None:
obj = self.zero()
for i in range(n):
value = self.add_variable(lower_bound = lower_bound,
upper_bound = upper_bound,
binary = binary,
continuous = continuous,
integer = integer,
obj = obj,
name = None if names is None else names[i])
return value
@classmethod
def _test_add_variables(cls, tester=None, **options):
"""
Run tests on the method :meth:`.add_linear_constraints`.
TESTS::
sage: from sage.numerical.backends.generic_backend import GenericBackend
sage: p = GenericBackend()
sage: p._test_add_variables()
Traceback (most recent call last):
...
NotImplementedError
"""
p = cls() # fresh instance of the backend
if tester is None:
tester = p._tester(**options)
# Test from CVXOPT interface:
ncols_added = 5
ncols_before = p.ncols()
add_variables_result = p.add_variables(ncols_added)
ncols_after = p.ncols()
tester.assertEqual(ncols_after, ncols_before+ncols_added, "Added the wrong number of columns")
# Test from CVXOPT interface, continued; edited to support InteractiveLPBackend
ncols_before = p.ncols()
try:
col_bounds = (-2.0, None)
add_variables_result = p.add_variables(2, lower_bound=col_bounds[0], upper_bound=col_bounds[1],
obj=42.0, names=['a','b'])
except NotImplementedError:
# The InteractiveLPBackend does not allow general variable bounds.
col_bounds = (0.0, None)
add_variables_result = p.add_variables(2, lower_bound=col_bounds[0], upper_bound=col_bounds[1],
obj=42.0, names=['a','b'])
ncols_after = p.ncols()
tester.assertAlmostEqual(p.col_bounds(ncols_before), col_bounds)
tester.assertEqual(p.col_name(ncols_before), 'a')
tester.assertAlmostEqual(p.objective_coefficient(ncols_before), 42.0)
cpdef set_variable_type(self, int variable, int vtype):
"""
Set the type of a variable
INPUT:
- ``variable`` (integer) -- the variable's id
- ``vtype`` (integer) :
* 1 Integer
* 0 Binary
* -1 Continuous
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.ncols() # optional - Nonexistent_LP_solver
0
sage: p.add_variable() # optional - Nonexistent_LP_solver
0
sage: p.set_variable_type(0,1) # optional - Nonexistent_LP_solver
sage: p.is_variable_integer(0) # optional - Nonexistent_LP_solver
True
"""
raise NotImplementedError()
cpdef set_sense(self, int sense):
"""
Set the direction (maximization/minimization).
INPUT:
- ``sense`` (integer) :
* +1 => Maximization
* -1 => Minimization
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.is_maximization() # optional - Nonexistent_LP_solver
True
sage: p.set_sense(-1) # optional - Nonexistent_LP_solver
sage: p.is_maximization() # optional - Nonexistent_LP_solver
False
"""
raise NotImplementedError()
@classmethod
def _test_sense(cls, tester=None, **options):
"""
Run tests on `set_sense` and `is_maximization`.
TESTS::
sage: from sage.numerical.backends.generic_backend import GenericBackend
sage: p = GenericBackend()
sage: p._test_sense() # optional - Nonexistent_LP_solver
Exception NotImplementedError ...
"""
p = cls() # fresh instance of the backend
if tester is None:
tester = p._tester(**options)
tester.assertEqual(p.is_maximization(), True)
tester.assertIsNone(p.set_sense(-1))
tester.assertEqual(p.is_maximization(), False)
tester.assertIsNone(p.set_sense(1))
tester.assertEqual(p.is_maximization(), True)
cpdef objective_coefficient(self, int variable, coeff=None):
"""
Set or get the coefficient of a variable in the objective
function
INPUT:
- ``variable`` (integer) -- the variable's id
- ``coeff`` (double) -- its coefficient
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.add_variable() # optional - Nonexistent_LP_solver
0
sage: p.objective_coefficient(0) # optional - Nonexistent_LP_solver
0.0
sage: p.objective_coefficient(0,2) # optional - Nonexistent_LP_solver
sage: p.objective_coefficient(0) # optional - Nonexistent_LP_solver
2.0
"""
raise NotImplementedError()
cpdef objective_constant_term(self, d=None):
"""
Set or get the constant term in the objective function
INPUT:
- ``d`` (double) -- its coefficient. If `None` (default), return the current value.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.objective_constant_term() # optional - Nonexistent_LP_solver
0.0
sage: p.objective_constant_term(42) # optional - Nonexistent_LP_solver
sage: p.objective_constant_term() # optional - Nonexistent_LP_solver
42.0
"""
if d is None:
return self.obj_constant_term
else:
self.obj_constant_term = d
cpdef set_objective(self, list coeff, d = 0.0):
"""
Set the objective function.
INPUT:
- ``coeff`` -- a list of real values, whose i-th element is the
coefficient of the i-th variable in the objective function.
- ``d`` (double) -- the constant term in the linear function (set to `0` by default)
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.add_variables(5) # optional - Nonexistent_LP_solver
4
sage: p.set_objective([1, 1, 2, 1, 3]) # optional - Nonexistent_LP_solver
sage: [p.objective_coefficient(x) for x in range(5)] # optional - Nonexistent_LP_solver
[1.0, 1.0, 2.0, 1.0, 3.0]
Constants in the objective function are respected::
sage: p = MixedIntegerLinearProgram(solver='Nonexistent_LP_solver') # optional - Nonexistent_LP_solver
sage: x,y = p[0], p[1] # optional - Nonexistent_LP_solver
sage: p.add_constraint(2*x + 3*y, max = 6) # optional - Nonexistent_LP_solver
sage: p.add_constraint(3*x + 2*y, max = 6) # optional - Nonexistent_LP_solver
sage: p.set_objective(x + y + 7) # optional - Nonexistent_LP_solver
sage: p.set_integer(x); p.set_integer(y) # optional - Nonexistent_LP_solver
sage: p.solve() # optional - Nonexistent_LP_solver
9.0
"""
raise NotImplementedError()
cpdef set_verbosity(self, int level):
"""
Set the log (verbosity) level
INPUT:
- ``level`` (integer) -- From 0 (no verbosity) to 3.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.set_verbosity(2) # optional - Nonexistent_LP_solver
"""
raise NotImplementedError()
cpdef remove_constraint(self, int i):
r"""
Remove a constraint.
INPUT:
- ``i`` -- index of the constraint to remove.
EXAMPLES::
sage: p = MixedIntegerLinearProgram(solver="Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: v = p.new_variable(nonnegative=True) # optional - Nonexistent_LP_solver
sage: x,y = v[0], v[1] # optional - Nonexistent_LP_solver
sage: p.add_constraint(2*x + 3*y, max = 6) # optional - Nonexistent_LP_solver
sage: p.add_constraint(3*x + 2*y, max = 6) # optional - Nonexistent_LP_solver
sage: p.set_objective(x + y + 7) # optional - Nonexistent_LP_solver
sage: p.set_integer(x); p.set_integer(y) # optional - Nonexistent_LP_solver
sage: p.solve() # optional - Nonexistent_LP_solver
9.0
sage: p.remove_constraint(0) # optional - Nonexistent_LP_solver
sage: p.solve() # optional - Nonexistent_LP_solver
10.0
sage: p.get_values([x,y]) # optional - Nonexistent_LP_solver
[0.0, 3.0]
"""
raise NotImplementedError()
cpdef remove_constraints(self, constraints):
r"""
Remove several constraints.
INPUT:
- ``constraints`` -- an iterable containing the indices of the rows to remove.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.add_constraint(p[0] + p[1], max = 10) # optional - Nonexistent_LP_solver
sage: p.remove_constraints([0]) # optional - Nonexistent_LP_solver
"""
if type(constraints) == int: self.remove_constraint(constraints)
cdef int last = self.nrows() + 1
for c in sorted(constraints, reverse=True):
if c != last:
self.remove_constraint(c)
last = c
cpdef add_linear_constraint(self, coefficients, lower_bound, upper_bound, name=None):
"""
Add a linear constraint.
INPUT:
- ``coefficients`` -- an iterable of pairs ``(i, v)``. In each
pair, ``i`` is a variable index (integer) and ``v`` is a
value (element of :meth:`base_ring`).
- ``lower_bound`` -- element of :meth:`base_ring` or
``None``. The lower bound.
- ``upper_bound`` -- element of :meth:`base_ring` or
``None``. The upper bound.
- ``name`` -- string or ``None``. Optional name for this row.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.add_variables(5) # optional - Nonexistent_LP_solver
4
sage: p.add_linear_constraint( zip(range(5), range(5)), 2.0, 2.0) # optional - Nonexistent_LP_solver
sage: p.row(0) # optional - Nonexistent_LP_solver
([0, 1, 2, 3, 4], [0.0, 1.0, 2.0, 3.0, 4.0])
sage: p.row_bounds(0) # optional - Nonexistent_LP_solver
(2.0, 2.0)
sage: p.add_linear_constraint( zip(range(5), range(5)), 1.0, 1.0, name='foo') # optional - Nonexistent_LP_solver
sage: p.row_name(1) # optional - Nonexistent_LP_solver
'foo'
"""
raise NotImplementedError('add_linear_constraint')
cpdef add_linear_constraint_vector(self, degree, coefficients, lower_bound, upper_bound, name=None):
"""
Add a vector-valued linear constraint.
.. NOTE::
This is the generic implementation, which will split the
vector-valued constraint into components and add these
individually. Backends are encouraged to replace it with
their own optimized implementation.
INPUT:
- ``degree`` -- integer. The vector degree, that is, the
number of new scalar constraints.
- ``coefficients`` -- an iterable of pairs ``(i, v)``. In each
pair, ``i`` is a variable index (integer) and ``v`` is a
vector (real and of length ``degree``).
- ``lower_bound`` -- either a vector or ``None``. The
component-wise lower bound.
- ``upper_bound`` -- either a vector or ``None``. The
component-wise upper bound.
- ``name`` -- string or ``None``. An optional name for all new
rows.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: coeffs = ([0, vector([1, 2])], [1, vector([2, 3])])
sage: upper = vector([5, 5])
sage: lower = vector([0, 0])
sage: p.add_variables(2) # optional - Nonexistent_LP_solver
1
sage: p.add_linear_constraint_vector(2, coeffs, lower, upper, 'foo') # optional - Nonexistent_LP_solver
"""
for d in range(degree):
coefficients_d = []
for i, c in coefficients:
coefficients_d.append((i, c[d]))
lower_bound_d = None if lower_bound is None else lower_bound[d]
upper_bound_d = None if upper_bound is None else upper_bound[d]
self.add_linear_constraint(coefficients_d, lower_bound_d, upper_bound_d, name=name)
@classmethod
def _test_add_linear_constraint_vector(cls, tester=None, **options):
"""
Run tests on the method :meth:`.add_linear_constraint_vector`.
TESTS::
sage: from sage.numerical.backends.generic_backend import GenericBackend
sage: p = GenericBackend()
sage: p._test_add_linear_constraint_vector()
Traceback (most recent call last):
...
NotImplementedError
"""
p = cls() # fresh instance of the backend
if tester is None:
tester = p._tester(**options)
from sage.modules.all import vector
# Ensure there are at least 2 variables
p.add_variables(2)
coeffs = ([0, vector([1, 2])], [1, vector([2, 3])])
upper = vector([5, 5])
lower = vector([0, 0])
try:
p.add_linear_constraint_vector(2, coeffs, lower, upper, 'foo')
except NotImplementedError:
# Ranged constraints are not supported by InteractiveLPBackend
lower = None
p.add_linear_constraint_vector(2, coeffs, lower, upper, 'foo')
# FIXME: Tests here. Careful what we expect regarding ranged constraints with some solvers.
cpdef add_col(self, list indices, list coeffs):
"""
Add a column.
INPUT:
- ``indices`` (list of integers) -- this list contains the
indices of the constraints in which the variable's
coefficient is nonzero
- ``coeffs`` (list of real values) -- associates a coefficient
to the variable in each of the constraints in which it
appears. Namely, the i-th entry of ``coeffs`` corresponds to
the coefficient of the variable in the constraint
represented by the i-th entry in ``indices``.
.. NOTE::
``indices`` and ``coeffs`` are expected to be of the same
length.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.ncols() # optional - Nonexistent_LP_solver
0
sage: p.nrows() # optional - Nonexistent_LP_solver
0
sage: p.add_linear_constraints(5, 0, None) # optional - Nonexistent_LP_solver
sage: p.add_col(list(range(5)), list(range(5))) # optional - Nonexistent_LP_solver
sage: p.nrows() # optional - Nonexistent_LP_solver
5
"""
raise NotImplementedError()
@classmethod
def _test_add_col(cls, tester=None, **options):
"""
Run tests on the method :meth:`.add_col`
TESTS::
sage: from sage.numerical.backends.generic_backend import GenericBackend
sage: p = GenericBackend()
sage: p._test_add_col()
Traceback (most recent call last):
...
NotImplementedError: ...
"""
p = cls() # fresh instance of the backend
if tester is None:
tester = p._tester(**options)
tester.assertIsNone(p.add_linear_constraints(5, 0, None))
tester.assertIsNone(p.add_col([0, 1, 2, 3, 4], [0, 1, 2, 3, 4]))
tester.assertEqual(p.nrows(), 5)
for 1 <= i <= 4:
tester.assertEqual(p.row(i), ([0], [i]))
cpdef add_linear_constraints(self, int number, lower_bound, upper_bound, names=None):
"""
Add ``'number`` linear constraints.
INPUT:
- ``number`` (integer) -- the number of constraints to add.
- ``lower_bound`` - a lower bound, either a real value or ``None``
- ``upper_bound`` - an upper bound, either a real value or ``None``
- ``names`` - an optional list of names (default: ``None``)
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.add_variables(5) # optional - Nonexistent_LP_solver
5
sage: p.add_linear_constraints(5, None, 2) # optional - Nonexistent_LP_solver
sage: p.row(4) # optional - Nonexistent_LP_solver
([], [])
sage: p.row_bounds(4) # optional - Nonexistent_LP_solver
(None, 2.0)
"""
cdef int i
for 0<= i<number:
self.add_linear_constraint([],lower_bound, upper_bound, name = (names[i] if names else None))
@classmethod
def _test_add_linear_constraints(cls, tester=None, **options):
"""
Run tests on the method :meth:`.add_linear_constraints`.
TESTS::
sage: from sage.numerical.backends.generic_backend import GenericBackend
sage: p = GenericBackend()
sage: p._test_add_linear_constraints()
...
Traceback (most recent call last):
...
NotImplementedError...
"""
p = cls() # fresh instance of the backend
if tester is None:
tester = p._tester(**options)
nrows_before = p.nrows()
nrows_added = 5
p.add_linear_constraints(nrows_added, None, 2)
nrows_after = p.nrows()
# Test correct number of rows
tester.assertEqual(nrows_after, nrows_before+nrows_added, "Added the wrong number of rows")
# Test contents of the new rows are correct (sparse zero)
for i in range(nrows_before, nrows_after):
tester.assertEqual(p.row(i), ([], []))
tester.assertEqual(p.row_bounds(i), (None, 2.0))
# Test from COINBackend.add_linear_constraints:
tester.assertIsNone(p.add_linear_constraints(2, None, 2, names=['foo', 'bar']))
tester.assertEqual(p.row_name(6), 'bar')
# Test that it did not add mysterious new variables:
tester.assertEqual(p.ncols(), 0)
cpdef int solve(self) except -1:
"""
Solve the problem.
.. NOTE::
This method raises ``MIPSolverException`` exceptions when
the solution can not be computed for any reason (none
exists, or the LP solver was not able to find it, etc...)
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.add_linear_constraints(5, 0, None) # optional - Nonexistent_LP_solver
sage: p.add_col(list(range(5)), list(range(5))) # optional - Nonexistent_LP_solver
sage: p.solve() # optional - Nonexistent_LP_solver
0
sage: p.objective_coefficient(0,1) # optional - Nonexistent_LP_solver
sage: p.solve() # optional - Nonexistent_LP_solver
Traceback (most recent call last):
...
MIPSolverException: ...
"""
raise NotImplementedError()
## Any test methods involving calls to 'solve' are set up as class methods,
## which make a fresh instance of the backend.
@classmethod
def _test_solve(cls, tester=None, **options):
"""
Trivial test for the solve method.
TESTS::
sage: from sage.numerical.backends.generic_backend import GenericBackend
sage: p = GenericBackend()
sage: p._test_solve()
Traceback (most recent call last):
...
NotImplementedError: ...
"""
p = cls() # fresh instance of the backend
if tester is None:
tester = p._tester(**options)
# From doctest of GenericBackend.solve:
tester.assertIsNone(p.add_linear_constraints(5, 0, None))
tester.assertIsNone(p.add_col(list(xrange(5)), list(xrange(5))))
tester.assertEqual(p.solve(), 0)
tester.assertIsNone(p.objective_coefficient(0,1))
from sage.numerical.mip import MIPSolverException
#with tester.assertRaisesRegexp(MIPSolverException, "unbounded") as cm: ## --- too specific
with tester.assertRaises(MIPSolverException) as cm: # unbounded
p.solve()
cpdef get_objective_value(self):
"""
Return the value of the objective function.
.. NOTE::
Behavior is undefined unless ``solve`` has been called before.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.add_variables(2) # optional - Nonexistent_LP_solver
1
sage: p.add_linear_constraint([(0,1), (1,2)], None, 3) # optional - Nonexistent_LP_solver
sage: p.set_objective([2, 5]) # optional - Nonexistent_LP_solver
sage: p.solve() # optional - Nonexistent_LP_solver
0
sage: p.get_objective_value() # optional - Nonexistent_LP_solver
7.5
sage: p.get_variable_value(0) # optional - Nonexistent_LP_solver
0.0
sage: p.get_variable_value(1) # optional - Nonexistent_LP_solver
1.5
"""
raise NotImplementedError()
cpdef best_known_objective_bound(self):
r"""
Return the value of the currently best known bound.
This method returns the current best upper (resp. lower) bound on the
optimal value of the objective function in a maximization
(resp. minimization) problem. It is equal to the output of
:meth:`get_objective_value` if the MILP found an optimal solution, but
it can differ if it was interrupted manually or after a time limit (cf
:meth:`solver_parameter`).
.. NOTE::
Has no meaning unless ``solve`` has been called before.
EXAMPLES::
sage: p = MixedIntegerLinearProgram(solver="Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: b = p.new_variable(binary=True) # optional - Nonexistent_LP_solver
sage: for u,v in graphs.CycleGraph(5).edges(labels=False): # optional - Nonexistent_LP_solver
....: p.add_constraint(b[u]+b[v]<=1) # optional - Nonexistent_LP_solver
sage: p.set_objective(p.sum(b[x] for x in range(5))) # optional - Nonexistent_LP_solver
sage: p.solve() # optional - Nonexistent_LP_solver
2.0
sage: pb = p.get_backend() # optional - Nonexistent_LP_solver
sage: pb.get_objective_value() # optional - Nonexistent_LP_solver
2.0
sage: pb.best_known_objective_bound() # optional - Nonexistent_LP_solver
2.0
"""
raise NotImplementedError()
cpdef get_relative_objective_gap(self):
r"""
Return the relative objective gap of the best known solution.
For a minimization problem, this value is computed by
`(\texttt{bestinteger} - \texttt{bestobjective}) / (1e-10 +
|\texttt{bestobjective}|)`, where ``bestinteger`` is the value returned
by :meth:`~MixedIntegerLinearProgram.get_objective_value` and
``bestobjective`` is the value returned by
:meth:`~MixedIntegerLinearProgram.best_known_objective_bound`. For a
maximization problem, the value is computed by `(\texttt{bestobjective}
- \texttt{bestinteger}) / (1e-10 + |\texttt{bestobjective}|)`.
.. NOTE::
Has no meaning unless ``solve`` has been called before.
EXAMPLES::
sage: p = MixedIntegerLinearProgram(solver="Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: b = p.new_variable(binary=True) # optional - Nonexistent_LP_solver
sage: for u,v in graphs.CycleGraph(5).edges(labels=False): # optional - Nonexistent_LP_solver
....: p.add_constraint(b[u]+b[v]<=1) # optional - Nonexistent_LP_solver
sage: p.set_objective(p.sum(b[x] for x in range(5))) # optional - Nonexistent_LP_solver
sage: p.solve() # optional - Nonexistent_LP_solver
2.0
sage: pb = p.get_backend() # optional - Nonexistent_LP_solver
sage: pb.get_objective_value() # optional - Nonexistent_LP_solver
2.0
sage: pb.get_relative_objective_gap() # optional - Nonexistent_LP_solver
0.0
"""
raise NotImplementedError()
cpdef get_variable_value(self, int variable):
"""
Return the value of a variable given by the solver.
.. NOTE::
Behavior is undefined unless ``solve`` has been called before.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.add_variables(2) # optional - Nonexistent_LP_solver
1
sage: p.add_linear_constraint([(0,1), (1, 2)], None, 3) # optional - Nonexistent_LP_solver
sage: p.set_objective([2, 5]) # optional - Nonexistent_LP_solver
sage: p.solve() # optional - Nonexistent_LP_solver
0
sage: p.get_objective_value() # optional - Nonexistent_LP_solver
7.5
sage: p.get_variable_value(0) # optional - Nonexistent_LP_solver
0.0
sage: p.get_variable_value(1) # optional - Nonexistent_LP_solver
1.5
"""
raise NotImplementedError()
cpdef int ncols(self):
"""
Return the number of columns/variables.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.ncols() # optional - Nonexistent_LP_solver
0
sage: p.add_variables(2) # optional - Nonexistent_LP_solver
1
sage: p.ncols() # optional - Nonexistent_LP_solver
2
"""
raise NotImplementedError()
def _test_ncols_nonnegative(self, **options):
tester = self._tester(**options)
p = self
tester.assertGreaterEqual(self.ncols(), 0)
cpdef int nrows(self):
"""
Return the number of rows/constraints.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.nrows() # optional - Nonexistent_LP_solver
0
sage: p.add_linear_constraints(2, 2.0, None) # optional - Nonexistent_LP_solver
sage: p.nrows() # optional - Nonexistent_LP_solver
2
"""
raise NotImplementedError()
cpdef bint is_maximization(self):
"""
Test whether the problem is a maximization
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.is_maximization() # optional - Nonexistent_LP_solver
True
sage: p.set_sense(-1) # optional - Nonexistent_LP_solver
sage: p.is_maximization() # optional - Nonexistent_LP_solver
False
"""
raise NotImplementedError()
cpdef problem_name(self, char * name = NULL):
"""
Return or define the problem's name
INPUT:
- ``name`` (``char *``) -- the problem's name. When set to
``NULL`` (default), the method returns the problem's name.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.problem_name("There once was a french fry") # optional - Nonexistent_LP_solver
sage: print(p.problem_name()) # optional - Nonexistent_LP_solver
There once was a french fry
"""
raise NotImplementedError()
cpdef write_lp(self, char * name):
"""
Write the problem to a ``.lp`` file
INPUT:
- ``filename`` (string)
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.add_variables(2) # optional - Nonexistent_LP_solver
2
sage: p.add_linear_constraint([(0, 1], (1, 2)], None, 3) # optional - Nonexistent_LP_solver
sage: p.set_objective([2, 5]) # optional - Nonexistent_LP_solver
sage: p.write_lp(os.path.join(SAGE_TMP, "lp_problem.lp")) # optional - Nonexistent_LP_solver
"""
raise NotImplementedError()
cpdef write_mps(self, char * name, int modern):
"""
Write the problem to a ``.mps`` file
INPUT:
- ``filename`` (string)
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: p.add_variables(2) # optional - Nonexistent_LP_solver
2
sage: p.add_linear_constraint([(0, 1), (1, 2)], None, 3) # optional - Nonexistent_LP_solver
sage: p.set_objective([2, 5]) # optional - Nonexistent_LP_solver
sage: p.write_lp(os.path.join(SAGE_TMP, "lp_problem.lp")) # optional - Nonexistent_LP_solver
"""
raise NotImplementedError()
cpdef copy(self):
"""
Returns a copy of self.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = MixedIntegerLinearProgram(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: b = p.new_variable() # optional - Nonexistent_LP_solver
sage: p.add_constraint(b[1] + b[2] <= 6) # optional - Nonexistent_LP_solver
sage: p.set_objective(b[1] + b[2]) # optional - Nonexistent_LP_solver
sage: copy(p).solve() # optional - Nonexistent_LP_solver
6.0
"""
return self.__copy__()
# Override this method in backends.
cpdef __copy__(self):
"""
Returns a copy of self.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = MixedIntegerLinearProgram(solver = "Nonexistent_LP_solver") # optional - Nonexistent_LP_solver
sage: b = p.new_variable() # optional - Nonexistent_LP_solver
sage: p.add_constraint(b[1] + b[2] <= 6) # optional - Nonexistent_LP_solver
sage: p.set_objective(b[1] + b[2]) # optional - Nonexistent_LP_solver
sage: cp = copy(p.get_backend()) # optional - Nonexistent_LP_solver
sage: cp.solve() # optional - Nonexistent_LP_solver
0
sage: cp.get_objective_value() # optional - Nonexistent_LP_solver
6.0
"""
raise NotImplementedError()
def __deepcopy__(self, memo={}):
"""
Return a deep copy of ``self``.