crosstyan
6034175a10
- Rename `_wrap` to `_cvx_opt_wrap` for consistency - Update import paths in solver module - Add type hints to `solution_mat_clusters` method - Improve type annotations in `solve` method
250 lines
6.6 KiB
Python
250 lines
6.6 KiB
Python
"""
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See also:
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https://github.com/cvxopt/cvxopt/blob/master/src/C/glpk.c
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"""
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from typing import Tuple, Union, Literal, Optional, Dict, Any, Set, overload, TypedDict
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from cvxopt import glpk # type: ignore
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from . import Matrix, SparseMatrix
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CvxMatLike = Union[Matrix, SparseMatrix]
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CvxBool = Literal["GLP_ON", "GLP_OFF"]
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class GLPKOptions(TypedDict, total=False):
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# Common parameters
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msg_lev: Literal["GLP_MSG_OFF", "GLP_MSG_ERR", "GLP_MSG_ON", "GLP_MSG_ALL"]
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presolve: CvxBool
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tm_lim: int
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out_frq: int
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out_dly: int
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# LP-specific parameters
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meth: Literal["GLP_PRIMAL", "GLP_DUAL", "GLP_DUALP"]
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pricing: Literal["GLP_PT_STD", "GLP_PT_PSE"]
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r_test: Literal["GLP_RT_STD", "GLP_RT_HAR"]
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tol_bnd: float
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tol_dj: float
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tol_piv: float
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obj_ll: float
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obj_ul: float
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it_lim: int
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# MILP-specific parameters
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br_tech: Literal[
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"GLP_BR_FFV", "GLP_BR_LFV", "GLP_BR_MFV", "GLP_BR_DTH", "GLP_BR_PCH"
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]
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bt_tech: Literal["GLP_BT_DFS", "GLP_BT_BFS", "GLP_BT_BLB", "GLP_BT_BPH"]
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pp_tech: Literal["GLP_PP_NONE", "GLP_PP_ROOT", "GLP_PP_ALL"]
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fp_heur: CvxBool
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gmi_cuts: CvxBool
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mir_cuts: CvxBool
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cov_cuts: CvxBool
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clq_cuts: CvxBool
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tol_int: float
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tol_obj: float
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mip_gap: float
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cb_size: int
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binarize: CvxBool
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StatusLP = Literal["optimal", "primal infeasible", "dual infeasible", "unknown"]
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StatusILP = Literal[
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"optimal",
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"feasible",
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"undefined",
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"invalid formulation",
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"infeasible problem",
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"LP relaxation is primal infeasible",
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"LP relaxation is dual infeasible",
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"unknown",
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]
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@overload
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def lp(
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c: Matrix,
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G: CvxMatLike,
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h: Matrix,
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) -> Tuple[StatusLP, Optional[Matrix], Optional[Matrix]]:
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"""
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(status, x, z) = lp(c, G, h)
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PURPOSE
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(status, x, z) = lp(c, G, h) solves the pair of primal and dual LPs
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minimize c'*x maximize -h'*z
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subject to G*x <= h subject to G'*z + c = 0
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z >= 0.
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ARGUMENTS
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c nx1 dense 'd' matrix with n>=1
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G mxn dense or sparse 'd' matrix with m>=1
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h mx1 dense 'd' matrix
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status 'optimal', 'primal infeasible', 'dual infeasible'
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or 'unknown'
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x if status is 'optimal', a primal optimal solution;
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None otherwise
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z if status is 'optimal', the dual optimal solution;
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None otherwise
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"""
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@overload
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def lp(
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c: Matrix,
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G: CvxMatLike,
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h: Matrix,
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A: CvxMatLike,
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b: Matrix,
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) -> Tuple[StatusLP, Optional[Matrix], Optional[Matrix], Optional[Matrix]]:
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"""
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(status, x, z, y) = lp(c, G, h, A, b)
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PURPOSE
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(status, x, z, y) = lp(c, G, h, A, b) solves the pair of primal and
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dual LPs
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minimize c'*x maximize -h'*z + b'*y
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subject to G*x <= h subject to G'*z + A'*y + c = 0
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A*x = b z >= 0.
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ARGUMENTS
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c nx1 dense 'd' matrix with n>=1
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G mxn dense or sparse 'd' matrix with m>=1
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h mx1 dense 'd' matrix
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A pxn dense or sparse 'd' matrix with p>=0
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b px1 dense 'd' matrix
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status 'optimal', 'primal infeasible', 'dual infeasible'
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or 'unknown'
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x if status is 'optimal', a primal optimal solution;
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None otherwise
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z,y if status is 'optimal', the dual optimal solution;
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None otherwise
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"""
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# https://cvxopt.org/userguide/coneprog.html#linear-programming
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def lp(
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c: Matrix,
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G: CvxMatLike,
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h: Matrix,
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A: Optional[CvxMatLike] = None,
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b: Optional[Matrix] = None,
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):
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"""
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(status, x, z, y) = lp(c, G, h, A, b)
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(status, x, z) = lp(c, G, h)
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PURPOSE
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(status, x, z, y) = lp(c, G, h, A, b) solves the pair of primal and
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dual LPs
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minimize c'*x maximize -h'*z + b'*y
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subject to G*x <= h subject to G'*z + A'*y + c = 0
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A*x = b z >= 0.
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(status, x, z) = lp(c, G, h) solves the pair of primal and dual LPs
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minimize c'*x maximize -h'*z
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subject to G*x <= h subject to G'*z + c = 0
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z >= 0.
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ARGUMENTS
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c nx1 dense 'd' matrix with n>=1
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G mxn dense or sparse 'd' matrix with m>=1
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h mx1 dense 'd' matrix
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A pxn dense or sparse 'd' matrix with p>=0
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b px1 dense 'd' matrix
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status 'optimal', 'primal infeasible', 'dual infeasible'
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or 'unknown'
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x if status is 'optimal', a primal optimal solution;
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None otherwise
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z,y if status is 'optimal', the dual optimal solution;
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None otherwise
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"""
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if A is None and b is None:
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return glpk.lp(c, G, h)
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return glpk.lp(c, G, h, A, b)
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def ilp(
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c: Matrix,
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G: CvxMatLike,
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h: Matrix,
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A: Optional[CvxMatLike] = None,
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b: Optional[Matrix] = None,
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I: Optional[Set[int]] = None,
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B: Optional[Set[int]] = None,
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) -> Tuple[StatusILP, Optional[Matrix]]:
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"""
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Solves a mixed integer linear program using GLPK.
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(status, x) = ilp(c, G, h, A, b, I, B)
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PURPOSE
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Solves the mixed integer linear programming problem
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minimize c'*x
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subject to G*x <= h
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A*x = b
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x[k] is integer for k in I
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x[k] is binary for k in B
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ARGUMENTS
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c nx1 dense 'd' matrix with n>=1
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G mxn dense or sparse 'd' matrix with m>=1
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h mx1 dense 'd' matrix
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A pxn dense or sparse 'd' matrix with p>=0
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b px1 dense 'd' matrix
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I set of indices of integer variables
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B set of indices of binary variables
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status if status is 'optimal', 'feasible', or 'undefined',
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a value of x is returned and the status string
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gives the status of x. Other possible values of
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status are: 'invalid formulation',
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'infeasible problem', 'LP relaxation is primal
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infeasible', 'LP relaxation is dual infeasible',
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'unknown'.
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x a (sub-)optimal solution if status is 'optimal',
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'feasible', or 'undefined'. None otherwise
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"""
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return glpk.ilp(c, G, h, A, b, I, B)
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def set_global_options(options: GLPKOptions) -> None:
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glpk.options = options
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def get_global_options() -> GLPKOptions:
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return glpk.options
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