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glpk_backend.pyx
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glpk_backend.pyx
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"""
GLPK Backend
AUTHORS:
- Nathann Cohen (2010-10): initial implementation
- John Perry (2012-01): glp_simplex preprocessing
- John Perry and Raniere Gaia Silva (2012-03): solver parameters
- Christian Kuper (2012-10): Additions for sensitivity analysis
"""
# ****************************************************************************
# 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.
# https://www.gnu.org/licenses/
# ****************************************************************************
from libc.float cimport DBL_MAX
from libc.limits cimport INT_MAX
from cysignals.memory cimport sig_malloc, sig_free
from cysignals.signals cimport sig_on, sig_off
from memory_allocator cimport MemoryAllocator
from sage.cpython.string cimport char_to_str, str_to_bytes
from sage.cpython.string import FS_ENCODING
from sage.numerical.mip import MIPSolverException
from sage.libs.glpk.constants cimport *
from sage.libs.glpk.lp cimport *
cdef class GLPKBackend(GenericBackend):
"""
MIP Backend that uses the GLPK solver.
"""
def __cinit__(self, maximization = True):
"""
Constructor.
EXAMPLES::
sage: p = MixedIntegerLinearProgram(solver='GLPK')
"""
self.lp = glp_create_prob()
self.simplex_or_intopt = glp_simplex_then_intopt
self.smcp = <glp_smcp* > sig_malloc(sizeof(glp_smcp))
glp_init_smcp(self.smcp)
self.iocp = <glp_iocp* > sig_malloc(sizeof(glp_iocp))
glp_init_iocp(self.iocp)
self.iocp.cb_func = glp_callback # callback function
self.iocp.cb_info = <void *> &(self.search_tree_data) # callback data
self.iocp.presolve = GLP_ON
self.set_verbosity(0)
self.obj_constant_term = 0.0
if maximization:
self.set_sense(+1)
else:
self.set_sense(-1)
cpdef int add_variable(self, lower_bound=0.0, upper_bound=None, binary=False, continuous=False, integer=False, obj=0.0, name=None) except -1:
"""
Add a variable.
This amounts to adding a new column to the matrix. By default,
the variable is both positive, real and the coefficient in the
objective function is 0.0.
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 continuous (default: ``True``)
- ``integer`` -- ``True`` if the variable is integral (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 = "GLPK")
sage: p.ncols()
0
sage: p.add_variable()
0
sage: p.ncols()
1
sage: p.add_variable(binary=True)
1
sage: p.add_variable(lower_bound=-2.0, integer=True)
2
sage: p.add_variable(continuous=True, integer=True)
Traceback (most recent call last):
...
ValueError: ...
sage: p.add_variable(name='x', obj=1.0)
3
sage: p.col_name(3)
'x'
sage: p.objective_coefficient(3)
1.0
"""
cdef int vtype = int(bool(binary)) + int(bool(continuous)) + int(bool(integer))
if vtype == 0:
continuous = True
elif vtype != 1:
raise ValueError("Exactly one parameter of 'binary', 'integer' and 'continuous' must be 'True'.")
glp_add_cols(self.lp, 1)
cdef int n_var = glp_get_num_cols(self.lp)
self.variable_lower_bound(n_var - 1, lower_bound)
self.variable_upper_bound(n_var - 1, upper_bound)
if continuous:
glp_set_col_kind(self.lp, n_var, GLP_CV)
elif binary:
glp_set_col_kind(self.lp, n_var, GLP_BV)
elif integer:
glp_set_col_kind(self.lp, n_var, GLP_IV)
if name is not None:
glp_set_col_name(self.lp, n_var, str_to_bytes(name))
if obj:
self.objective_coefficient(n_var - 1, obj)
return n_var - 1
cpdef int add_variables(self, int number, lower_bound=0.0, upper_bound=None, binary=False, continuous=False, integer=False, obj=0.0, names=None) except -1:
"""
Add ``number`` new variables.
This amounts to adding new columns to the matrix. By default,
the variables are both positive, real and their coefficient in
the objective function is 0.0.
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`` -- coefficient of all variables in the objective function (default: 0.0)
- ``names`` -- 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 = "GLPK")
sage: p.ncols()
0
sage: p.add_variables(5)
4
sage: p.ncols()
5
sage: p.add_variables(2, lower_bound=-2.0, integer=True, obj=42.0, names=['a','b'])
6
TESTS:
Check that arguments are used::
sage: p.col_bounds(5) # tol 1e-8
(-2.0, None)
sage: p.is_variable_integer(5)
True
sage: p.col_name(5)
'a'
sage: p.objective_coefficient(5)
42.0
"""
cdef int vtype = int(bool(binary)) + int(bool(continuous)) + int(bool(integer))
if vtype == 0:
continuous = True
elif vtype != 1:
raise ValueError("Exactly one parameter of 'binary', 'integer' and 'continuous' must be 'True'.")
glp_add_cols(self.lp, number)
cdef int n_var
n_var = glp_get_num_cols(self.lp)
cdef int i
for 0<= i < number:
self.variable_lower_bound(n_var - i - 1, lower_bound)
self.variable_upper_bound(n_var - i - 1, upper_bound)
if continuous:
glp_set_col_kind(self.lp, n_var - i, GLP_CV)
elif binary:
glp_set_col_kind(self.lp, n_var - i, GLP_BV)
elif integer:
glp_set_col_kind(self.lp, n_var - i, GLP_IV)
if obj:
self.objective_coefficient(n_var - i - 1, obj)
if names is not None:
glp_set_col_name(self.lp, n_var - i,
str_to_bytes(names[number - i - 1]))
return n_var - 1
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 Real
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "GLPK")
sage: p.ncols()
0
sage: p.add_variable()
0
sage: p.set_variable_type(0,1)
sage: p.is_variable_integer(0)
True
TESTS:
We sanity check the input that will be passed to GLPK::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver='GLPK')
sage: p.set_variable_type(2,0)
Traceback (most recent call last):
...
ValueError: invalid variable index 2
"""
if variable < 0 or variable > (self.ncols() - 1):
raise ValueError("invalid variable index %d" % variable)
if vtype==1:
glp_set_col_kind(self.lp, variable+1, GLP_IV)
elif vtype==0:
glp_set_col_kind(self.lp, variable+1, GLP_BV)
else:
glp_set_col_kind(self.lp, variable+1, GLP_CV)
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 = "GLPK")
sage: p.is_maximization()
True
sage: p.set_sense(-1)
sage: p.is_maximization()
False
"""
if sense == 1:
glp_set_obj_dir(self.lp, GLP_MAX)
else:
glp_set_obj_dir(self.lp, GLP_MIN)
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 or ``None`` for
reading (default: ``None``)
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "GLPK")
sage: p.add_variable()
0
sage: p.objective_coefficient(0)
0.0
sage: p.objective_coefficient(0,2)
sage: p.objective_coefficient(0)
2.0
TESTS:
We sanity check the input that will be passed to GLPK::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver='GLPK')
sage: p.objective_coefficient(2)
Traceback (most recent call last):
...
ValueError: invalid variable index 2
"""
if variable < 0 or variable > (self.ncols() - 1):
raise ValueError("invalid variable index %d" % variable)
if coeff is None:
return glp_get_obj_coef(self.lp, variable + 1)
else:
glp_set_obj_coef(self.lp, variable + 1, coeff)
cpdef problem_name(self, name=None):
"""
Return or define the problem's name.
INPUT:
- ``name`` -- string; the problem's name. When set to
``None`` (default), the method returns the problem's name.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "GLPK")
sage: p.problem_name("There once was a french fry")
sage: print(p.problem_name())
There once was a french fry
"""
cdef char * n
if name is None:
n = <char *> glp_get_prob_name(self.lp)
if n == NULL:
return ""
else:
return char_to_str(n)
else:
name = str_to_bytes(name)
if len(name) > 255:
raise ValueError("Problem name for GLPK must not be longer than 255 characters.")
glp_set_prob_name(self.lp, name)
cpdef set_objective(self, list coeff, d=0.0):
"""
Set the objective function.
INPUT:
- ``coeff`` -- 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 = "GLPK")
sage: p.add_variables(5)
4
sage: p.set_objective([1, 1, 2, 1, 3])
sage: [p.objective_coefficient(x) for x in range(5)]
[1.0, 1.0, 2.0, 1.0, 3.0]
"""
cdef int i
for i,v in enumerate(coeff):
glp_set_obj_coef(self.lp, i+1, v)
glp_set_obj_coef(self.lp, 0, d)
self.obj_constant_term = d
cpdef set_verbosity(self, int level):
"""
Set the verbosity level.
INPUT:
- ``level`` -- integer; from 0 (no verbosity) to 3
EXAMPLES::
sage: p.<x> = MixedIntegerLinearProgram(solver='GLPK')
sage: p.add_constraint(10 * x[0] <= 1)
sage: p.add_constraint(5 * x[1] <= 1)
sage: p.set_objective(x[0] + x[1])
sage: p.solve()
0.30000000000000004
sage: p.get_backend().set_verbosity(3)
sage: p.solver_parameter("simplex_or_intopt", "intopt_only")
sage: p.solve()
GLPK Integer Optimizer...
2 rows, 2 columns, 2 non-zeros
0 integer variables, none of which are binary
Preprocessing...
Objective value = 3.000000000e-01
INTEGER OPTIMAL SOLUTION FOUND BY MIP PREPROCESSOR
0.30000000000000004
::
sage: p.<x> = MixedIntegerLinearProgram(solver='GLPK/exact')
sage: p.add_constraint(10 * x[0] <= 1)
sage: p.add_constraint(5 * x[1] <= 1)
sage: p.set_objective(x[0] + x[1])
sage: p.solve() # tol 1e-14
0.3
sage: p.get_backend().set_verbosity(2)
sage: p.solve() # tol 1e-14
* 2: objval = 0.3 (0)
* 2: objval = 0.3 (0)
0.3
sage: p.get_backend().set_verbosity(3)
sage: p.solve() # tol 1e-14
glp_exact: 2 rows, 2 columns, 2 non-zeros
...
* 2: objval = 0.3 (0)
* 2: objval = 0.3 (0)
OPTIMAL SOLUTION FOUND
0.3
"""
if level == 0:
self.iocp.msg_lev = GLP_MSG_OFF
self.smcp.msg_lev = GLP_MSG_OFF
elif level == 1:
self.iocp.msg_lev = GLP_MSG_ERR
self.smcp.msg_lev = GLP_MSG_ERR
elif level == 2:
self.iocp.msg_lev = GLP_MSG_ON
self.smcp.msg_lev = GLP_MSG_ON
else:
self.iocp.msg_lev = GLP_MSG_ALL
self.smcp.msg_lev = GLP_MSG_ALL
cpdef remove_constraint(self, int i):
r"""
Remove a constraint from ``self``.
INPUT:
- ``i`` -- index of the constraint to remove
EXAMPLES::
sage: p = MixedIntegerLinearProgram(solver='GLPK')
sage: x, y = p['x'], p['y']
sage: p.add_constraint(2*x + 3*y <= 6)
sage: p.add_constraint(3*x + 2*y <= 6)
sage: p.add_constraint(x >= 0)
sage: p.set_objective(x + y + 7)
sage: p.set_integer(x); p.set_integer(y)
sage: p.solve()
9.0
sage: p.remove_constraint(0)
sage: p.solve()
10.0
Removing fancy constraints does not make Sage crash::
sage: MixedIntegerLinearProgram(solver = "GLPK").remove_constraint(-2)
Traceback (most recent call last):
...
ValueError: The constraint's index i must satisfy 0 <= i < number_of_constraints
"""
cdef int rows[2]
if i < 0 or i >= glp_get_num_rows(self.lp):
raise ValueError("The constraint's index i must satisfy 0 <= i < number_of_constraints")
rows[1] = i + 1
glp_del_rows(self.lp, 1, rows)
glp_std_basis(self.lp)
cpdef remove_constraints(self, constraints):
r"""
Remove several constraints.
INPUT:
- ``constraints`` -- an iterable containing the indices of the rows to remove
EXAMPLES::
sage: p = MixedIntegerLinearProgram(solver='GLPK')
sage: x, y = p['x'], p['y']
sage: p.add_constraint(2*x + 3*y <= 6)
sage: p.add_constraint(3*x + 2*y <= 6)
sage: p.add_constraint(x >= 0)
sage: p.set_objective(x + y + 7)
sage: p.set_integer(x); p.set_integer(y)
sage: p.solve()
9.0
sage: p.remove_constraints([0])
sage: p.solve()
10.0
sage: p.get_values([x,y])
[0.0, 3.0]
TESTS:
Removing fancy constraints does not make Sage crash::
sage: MixedIntegerLinearProgram(solver= "GLPK").remove_constraints([0, -2])
Traceback (most recent call last):
...
ValueError: The constraint's index i must satisfy 0 <= i < number_of_constraints
"""
cdef int i, c
cdef int m = len(constraints)
cdef int * rows = <int *>sig_malloc((m + 1) * sizeof(int *))
cdef int nrows = glp_get_num_rows(self.lp)
for i in range(m):
c = constraints[i]
if c < 0 or c >= nrows:
sig_free(rows)
raise ValueError("The constraint's index i must satisfy 0 <= i < number_of_constraints")
rows[i+1] = c + 1
glp_del_rows(self.lp, m, rows)
sig_free(rows)
glp_std_basis(self.lp)
cpdef add_linear_constraint(self, coefficients, lower_bound, upper_bound, name=None):
"""
Add a linear constraint.
INPUT:
- ``coefficients`` an iterable with ``(c,v)`` pairs where ``c``
is a variable index (integer) and ``v`` is a value (real
value).
- ``lower_bound`` -- a lower bound, either a real value or ``None``
- ``upper_bound`` -- an upper bound, either a real value or ``None``
- ``name`` -- an optional name for this row (default: ``None``)
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "GLPK")
sage: p.add_variables(5)
4
sage: p.add_linear_constraint(zip(range(5), range(5)), 2.0, 2.0)
sage: p.row(0)
([4, 3, 2, 1], [4.0, 3.0, 2.0, 1.0])
sage: p.row_bounds(0)
(2.0, 2.0)
sage: p.add_linear_constraint(zip(range(5), range(5)), 1.0, 1.0, name='foo')
sage: p.row_name(1)
'foo'
TESTS:
This used to crash Sage, but was fixed in :issue:`19525`::
sage: p = MixedIntegerLinearProgram(solver='glpk')
sage: q = MixedIntegerLinearProgram(solver='glpk')
sage: q.add_constraint(p.new_variable()[0] <= 1)
Traceback (most recent call last):
...
ValueError: invalid variable index 0
"""
if lower_bound is None and upper_bound is None:
raise ValueError("At least one of 'upper_bound' or 'lower_bound' must be set.")
# We're going to iterate through this more than once.
coefficients = list(coefficients)
for (index, _) in coefficients:
if index < 0 or index > (self.ncols() - 1):
raise ValueError("invalid variable index %d" % index)
glp_add_rows(self.lp, 1)
cdef int n = glp_get_num_rows(self.lp)
cdef MemoryAllocator mem = MemoryAllocator()
cdef int * row_i
cdef double * row_values
cdef int n_coeff = len(coefficients)
row_i = <int*>mem.allocarray(n_coeff + 1, sizeof(int))
row_values = <double*>mem.allocarray(n_coeff + 1, sizeof(double))
cdef Py_ssize_t i = 1
for c,v in coefficients:
row_i[i] = c+1
row_values[i] = v
i += 1
sig_on()
glp_set_mat_row(self.lp, n, n_coeff, row_i, row_values)
sig_off()
if upper_bound is not None and lower_bound is None:
glp_set_row_bnds(self.lp, n, GLP_UP, upper_bound, upper_bound)
elif lower_bound is not None and upper_bound is None:
glp_set_row_bnds(self.lp, n, GLP_LO, lower_bound, lower_bound)
elif upper_bound is not None and lower_bound is not None:
if lower_bound == upper_bound:
glp_set_row_bnds(self.lp, n, GLP_FX, lower_bound, upper_bound)
else:
glp_set_row_bnds(self.lp, n, GLP_DB, lower_bound, upper_bound)
if name is not None:
glp_set_row_name(self.lp, n, str_to_bytes(name))
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 = "GLPK")
sage: p.add_variables(5)
4
sage: p.add_linear_constraints(5, None, 2)
sage: p.row(4)
([], [])
sage: p.row_bounds(4)
(None, 2.0)
sage: p.add_linear_constraints(2, None, 2, names=['foo','bar'])
"""
if lower_bound is None and upper_bound is None:
raise ValueError("At least one of 'upper_bound' or 'lower_bound' must be set.")
glp_add_rows(self.lp, number)
cdef int n = glp_get_num_rows(self.lp)
cdef int i
for 0<= i < number:
if upper_bound is not None and lower_bound is None:
glp_set_row_bnds(self.lp, n-i, GLP_UP, upper_bound, upper_bound)
elif lower_bound is not None and upper_bound is None:
glp_set_row_bnds(self.lp, n-i, GLP_LO, lower_bound, lower_bound)
elif upper_bound is not None and lower_bound is not None:
if lower_bound == upper_bound:
glp_set_row_bnds(self.lp, n-i, GLP_FX, lower_bound, upper_bound)
else:
glp_set_row_bnds(self.lp, n-i, GLP_DB, lower_bound, upper_bound)
if names is not None:
glp_set_row_name(self.lp, n-i,
str_to_bytes(names[number-i-1]))
cpdef row(self, int index):
r"""
Return a row.
INPUT:
- ``index`` -- integer; the constraint's id
OUTPUT:
A pair ``(indices, coeffs)`` where ``indices`` lists the
entries whose coefficient is nonzero, and to which ``coeffs``
associates their coefficient on the model of the
``add_linear_constraint`` method.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "GLPK")
sage: p.add_variables(5)
4
sage: p.add_linear_constraint(list(zip(range(5), range(5))), 2, 2)
sage: p.row(0)
([4, 3, 2, 1], [4.0, 3.0, 2.0, 1.0])
sage: p.row_bounds(0)
(2.0, 2.0)
TESTS:
We sanity check the input that will be passed to GLPK::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver='GLPK')
sage: p.row(2)
Traceback (most recent call last):
...
ValueError: invalid row index 2
"""
if index < 0 or index > (self.nrows() - 1):
raise ValueError("invalid row index %d" % index)
cdef int n = glp_get_num_cols(self.lp)
cdef MemoryAllocator mem = MemoryAllocator()
cdef int * c_indices = <int*>mem.allocarray(n+1, sizeof(int))
cdef double * c_values = <double*>mem.allocarray(n+1, sizeof(double))
cdef list indices = []
cdef list values = []
cdef int i,j
i = glp_get_mat_row(self.lp, index + 1, c_indices, c_values)
for 0 < j <= i:
indices.append(c_indices[j]-1)
values.append(c_values[j])
return (indices, values)
cpdef row_bounds(self, int index):
"""
Return the bounds of a specific constraint.
INPUT:
- ``index`` -- integer; the constraint's id
OUTPUT:
A pair ``(lower_bound, upper_bound)``. Each of them can be set
to ``None`` if the constraint is not bounded in the
corresponding direction, and is a real value otherwise.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "GLPK")
sage: p.add_variables(5)
4
sage: p.add_linear_constraint(list(zip(range(5), range(5))), 2, 2)
sage: p.row(0)
([4, 3, 2, 1], [4.0, 3.0, 2.0, 1.0])
sage: p.row_bounds(0)
(2.0, 2.0)
TESTS:
We sanity check the input that will be passed to GLPK::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver='GLPK')
sage: p.row_bounds(2)
Traceback (most recent call last):
...
ValueError: invalid row index 2
"""
cdef double ub
cdef double lb
if index < 0 or index > (self.nrows() - 1):
raise ValueError("invalid row index %d" % index)
ub = glp_get_row_ub(self.lp, index + 1)
lb = glp_get_row_lb(self.lp, index +1)
return (
(lb if lb != -DBL_MAX else None),
(ub if ub != +DBL_MAX else None)
)
cpdef col_bounds(self, int index):
"""
Return the bounds of a specific variable.
INPUT:
- ``index`` -- integer; the variable's id
OUTPUT:
A pair ``(lower_bound, upper_bound)``. Each of them can be set
to ``None`` if the variable is not bounded in the
corresponding direction, and is a real value otherwise.
EXAMPLES::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver = "GLPK")
sage: p.add_variable()
0
sage: p.col_bounds(0)
(0.0, None)
sage: p.variable_upper_bound(0, 5)
sage: p.col_bounds(0)
(0.0, 5.0)
TESTS:
We sanity check the input that will be passed to GLPK::
sage: from sage.numerical.backends.generic_backend import get_solver
sage: p = get_solver(solver='GLPK')
sage: p.col_bounds(2)
Traceback (most recent call last):
...
ValueError: invalid column index 2
"""
cdef double ub
cdef double lb
if index < 0 or index > (self.ncols() - 1):
raise ValueError("invalid column index %d" % index)
ub = glp_get_col_ub(self.lp, index +1)
lb = glp_get_col_lb(self.lp, index +1)
return (
(lb if lb != -DBL_MAX else None),
(ub if ub != +DBL_MAX else None)
)
cpdef add_col(self, indices, 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 = "GLPK")
sage: p.ncols()
0
sage: p.nrows()
0
sage: p.add_linear_constraints(5, 0, None)
sage: p.add_col(range(5), range(5))
sage: p.nrows()
5
"""
glp_add_cols(self.lp, 1)
cdef int n = glp_get_num_cols(self.lp)
cdef int * col_i
cdef double * col_values
col_i = <int *> sig_malloc((len(indices)+1) * sizeof(int))
col_values = <double *> sig_malloc((len(indices)+1) * sizeof(double))
for i,v in enumerate(indices):
col_i[i+1] = v+1
for i,v in enumerate(coeffs):
col_values[i+1] = v
glp_set_mat_col(self.lp, n, len(indices), col_i, col_values)
glp_set_col_bnds(self.lp, n, GLP_LO, 0,0)
sig_free(col_i)
sig_free(col_values)
cpdef int solve(self) except -1:
"""
Solve the problem.
Sage uses GLPK's implementation of the branch-and-cut
algorithm (``glp_intopt``) to solve the mixed-integer linear
program. This algorithm can be requested explicitly by
setting the solver parameter "simplex_or_intopt" to
"intopt_only". By default, the simplex method will be used
first to detect pathological problems that the integer solver
cannot handle. If all variables are continuous, the integer
algorithm reduces to solving the linear program by the simplex
method.
EXAMPLES::
sage: lp = MixedIntegerLinearProgram(solver = 'GLPK', maximization = False)
sage: x, y = lp[0], lp[1]
sage: lp.add_constraint(-2*x + y <= 1)
sage: lp.add_constraint(x - y <= 1)
sage: lp.add_constraint(x + y >= 2)
sage: lp.set_objective(x + y)
sage: lp.set_integer(x)
sage: lp.set_integer(y)
sage: lp.solve()
2.0
sage: lp.get_values([x, y])
[1.0, 1.0]
.. NOTE::
This method raises ``MIPSolverException`` exceptions when
the solution cannot 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 = "GLPK")
sage: p.add_linear_constraints(5, 0, None)
sage: p.add_col(range(5), range(5))
sage: p.solve()
0
sage: p.objective_coefficient(0,1)
sage: p.solve()
Traceback (most recent call last):
...
MIPSolverException: ...
.. WARNING::
GLPK's ``glp_intopt`` sometimes fails catastrophically
when given a system it cannot solve (:issue:`12309`). It
can loop indefinitely, or just plain segfault. Upstream
considers this behavior "essentially innate" to the
current design, and suggests preprocessing with
``glp_simplex``, which is what SageMath does by default.
Set the ``simplex_or_intopt`` solver parameter to
``glp_intopt_only`` at your own risk.
EXAMPLES::
sage: lp = MixedIntegerLinearProgram(solver = "GLPK")
sage: v = lp.new_variable(nonnegative=True)
sage: lp.add_constraint(v[1] +v[2] -2.0 *v[3], max=-1.0)
sage: lp.add_constraint(v[0] -4.0/3 *v[1] +1.0/3 *v[2], max=-1.0/3)
sage: lp.add_constraint(v[0] +0.5 *v[1] -0.5 *v[2] +0.25 *v[3], max=-0.25)
sage: lp.solve()
0.0
sage: lp.add_constraint(v[0] +4.0 *v[1] -v[2] +v[3], max=-1.0)
sage: lp.solve()
Traceback (most recent call last):
...
MIPSolverException: GLPK: Problem has no feasible solution
If we switch to "simplex_only", the integrality constraints are ignored,
and we get an optimal solution to the continuous relaxation.
EXAMPLES::
sage: lp = MixedIntegerLinearProgram(solver = 'GLPK', maximization = False)
sage: x, y = lp[0], lp[1]
sage: lp.add_constraint(-2*x + y <= 1)
sage: lp.add_constraint(x - y <= 1)
sage: lp.add_constraint(x + y >= 2)
sage: lp.set_objective(x + y)
sage: lp.set_integer(x)
sage: lp.set_integer(y)
sage: lp.solver_parameter("simplex_or_intopt", "simplex_only") # use simplex only
sage: lp.solve()
2.0