gyf/CVTH3PE
1
0
Fork 0
forked from HQU-gxy/CVTH3PE
Code Pull Requests Activity

feat: Add CVXOPT solver infrastructure and VSCode settings

Browse Source 
- Add CVXOPT dependency to pyproject.toml and uv.lock
- Create solver module with GLPK-based integer linear programming solver
- Add VSCode Python analysis settings
- Implement matrix and sparse matrix wrappers for CVXOPT
- Add GLPK solver wrapper with type-safe interfaces
This commit is contained in:
crosstyan
2025-03-03 17:27:42 +08:00
parent f0f71f7f81
commit 39da7992d4
6 changed files with 702 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

227
app/solver/__init__.py Normal file
View File

@ -0,0 +1,227 @@
import itertools
from abc import abstractmethod
from collections import defaultdict
from typing import Tuple, override
import numpy as np
from app._typing import NDArray
from ._wrap import matrix, spmatrix
from ._wrap.glpk import ilp
from ._wrap.glpk import set_global_options as set_glpk_options
set_glpk_options({"msg_lev": "GLP_MSG_ERR"})
FROZEN_POS_EDGE = -1
FROZEN_NEG_EDGE = -2
INVALID_EDGE = -100
class _BIPSolver:
"""
Base class for BIP solvers
"""
min_affinity: float
max_affinity: float
def __init__(self, min_affinity: float = -np.inf, max_affinity: float = np.inf):
self.min_affinity = min_affinity
self.max_affinity = max_affinity
@staticmethod
def _create_bip(affinity_matrix: NDArray, min_affinity: float, max_affinity: float):
n_nodes = affinity_matrix.shape[0]
# mask for selecting pairs of nodes
triu_mask = np.triu(np.ones_like(affinity_matrix, dtype=bool), 1)
affinities = affinity_matrix[triu_mask]
frozen_pos_mask = affinities >= max_affinity
frozen_neg_mask = affinities <= min_affinity
unfrozen_mask = np.logical_not(frozen_pos_mask | frozen_neg_mask)
# generate objective coefficients
objective_coefficients = affinities[unfrozen_mask]
if len(objective_coefficients) == 0: # nio unfrozen edges
objective_coefficients = np.asarray([affinity_matrix[0, -1]])
unfrozen_mask = np.zeros_like(unfrozen_mask, dtype=np.bool)
unfrozen_mask[affinity_matrix.shape[1] - 1] = 1
# create matrix whose rows are the indices of the three edges in a
# constraint x_ij + x_ik - x_jk <= 1
constraints_edges_idx = []
if n_nodes >= 3:
edges_idx = np.empty_like(affinities, dtype=int)
edges_idx[frozen_pos_mask] = FROZEN_POS_EDGE
edges_idx[frozen_neg_mask] = FROZEN_NEG_EDGE
edges_idx[unfrozen_mask] = np.arange(len(objective_coefficients))
nodes_to_edge_matrix = np.empty_like(affinity_matrix, dtype=int)
nodes_to_edge_matrix.fill(INVALID_EDGE)
nodes_to_edge_matrix[triu_mask] = edges_idx
triplets = np.asarray(
tuple(itertools.combinations(range(n_nodes), 3)), dtype=int
)
constraints_edges_idx = np.zeros_like(triplets)
constraints_edges_idx[:, 0] = nodes_to_edge_matrix[
(triplets[:, 0], triplets[:, 1])
]
constraints_edges_idx[:, 1] = nodes_to_edge_matrix[
(triplets[:, 0], triplets[:, 2])
]
constraints_edges_idx[:, 2] = nodes_to_edge_matrix[
(triplets[:, 1], triplets[:, 2])
]
constraints_edges_idx = constraints_edges_idx[
np.any(constraints_edges_idx >= 0, axis=1)
]
if len(constraints_edges_idx) == 0: # no constraints
constraints_edges_idx = np.asarray([0, 0, 0], dtype=int).reshape(-1, 3)
# add remaining constraints by permutation
constraints_edges_idx = np.vstack(
(
constraints_edges_idx,
np.roll(constraints_edges_idx, 1, axis=1),
np.roll(constraints_edges_idx, 2, axis=1),
)
)
# clean redundant constraints
# x1 + x2 <= 2
constraints_edges_idx = constraints_edges_idx[
constraints_edges_idx[:, 2] != FROZEN_POS_EDGE
]
# x1 - x2 <= 1
constraints_edges_idx = constraints_edges_idx[
np.all(constraints_edges_idx[:, 0:2] != FROZEN_NEG_EDGE, axis=1)
]
if len(constraints_edges_idx) == 0: # no constraints
constraints_edges_idx = np.asarray([0, 0, 0], dtype=int).reshape(-1, 3)
# generate constraint coefficients
constraints_coefficients = np.ones_like(constraints_edges_idx)
constraints_coefficients[:, 2] = -1
# generate constraint upper bounds
upper_bounds = np.ones(len(constraints_coefficients), dtype=float)
upper_bounds -= np.sum(
constraints_coefficients * (constraints_edges_idx == FROZEN_POS_EDGE),
axis=1,
)
# flatten constraints data into sparse matrix format
constraints_idx = np.repeat(np.arange(len(constraints_edges_idx)), 3)
constraints_edges_idx = constraints_edges_idx.reshape(-1)
constraints_coefficients = constraints_coefficients.reshape(-1)
unfrozen_edges = constraints_edges_idx >= 0
constraints_idx = constraints_idx[unfrozen_edges]
constraints_edges_idx = constraints_edges_idx[unfrozen_edges]
constraints_coefficients = constraints_coefficients[unfrozen_edges]
return (
objective_coefficients,
unfrozen_mask,
frozen_pos_mask,
frozen_neg_mask,
(constraints_coefficients, constraints_idx, constraints_edges_idx),
upper_bounds,
)
@abstractmethod
def _solve_bip(self, objective_coefficients, sparse_constraints, upper_bounds): ...
@staticmethod
def solution_mat_clusters(solution_mat: NDArray):
n = solution_mat.shape[0]
labels = np.arange(1, n + 1)
for i in range(n):
for j in range(i + 1, n):
if solution_mat[i, j] > 0:
labels[j] = labels[i]
clusters = defaultdict(list)
for i, label in enumerate(labels):
clusters[label].append(i)
return list(clusters.values())
def solve(self, affinity_matrix, rtn_matrix=False):
n_nodes = affinity_matrix.shape[0]
if n_nodes <= 1:
solution_x, sol_matrix = (
np.asarray([], dtype=int),
np.asarray([0] * n_nodes, dtype=int),
)
sol_matrix = sol_matrix[:, None]
elif n_nodes == 2:
solution_matrix = np.zeros_like(affinity_matrix, dtype=int)
solution_matrix[0, 1] = affinity_matrix[0, 1] > 0
solution_matrix += solution_matrix.T
solution_x = (
[solution_matrix[0, 1]]
if self.min_affinity < affinity_matrix[0, 1] < self.max_affinity
else []
)
solution_x, sol_matrix = np.asarray(solution_x), solution_matrix
else:
# create BIP problem
(
objective_coefficients,
unfrozen_mask,
frozen_pos_mask,
frozen_neg_mask,
sparse_constraints,
upper_bounds,
) = self._create_bip(affinity_matrix, self.min_affinity, self.max_affinity)
# solve
solution_x = self._solve_bip(
objective_coefficients, sparse_constraints, upper_bounds
)
# solution to matrix
all_sols = np.zeros_like(unfrozen_mask, dtype=int)
all_sols[unfrozen_mask] = np.array(solution_x, dtype=int).reshape(-1)
all_sols[frozen_neg_mask] = 0
all_sols[frozen_pos_mask] = 1
sol_matrix = np.zeros_like(affinity_matrix, dtype=int)
sol_matrix[np.triu(np.ones([n_nodes, n_nodes], dtype=int), 1) > 0] = (
all_sols
)
sol_matrix += sol_matrix.T
clusters = self.solution_mat_clusters(sol_matrix)
if not rtn_matrix:
return clusters
return clusters, sol_matrix
class GLPKSolver(_BIPSolver):
def __init__(self, min_affinity=-np.inf, max_affinity=np.inf):
super().__init__(min_affinity, max_affinity)
@override
def _solve_bip(
self,
objective_coefficients: NDArray,
sparse_constraints: Tuple[NDArray, NDArray, NDArray],
upper_bounds: NDArray,
):
c = matrix(-objective_coefficients) # max -> min
# G * x <= h
G = spmatrix(
*sparse_constraints, size=(len(upper_bounds), len(objective_coefficients))
)
h = matrix(upper_bounds, tc="d")
status, solution = ilp(c, G, h, B=set(range(len(c))))
assert solution is not None, "Solver error: {}".format(status)
return np.asarray(solution, int).reshape(-1)