def test_min_problem():
"""
This function will test a minimization problem
"""
# Definimos y resolvemos problema con scipy
c_min_obj = [-1, -3]
A_min_obj = [[1, 1], [-1, 2]]
b_min_obj = [6, 8]
min_obj = linprog(c_min_obj, A_ub=A_min_obj, b_ub=b_min_obj).fun
coeff_obj = linprog(c_min_obj, A_ub=A_min_obj, b_ub=b_min_obj).x
# Definimos y resolvemos problema con nuestro paquete
n_var_approx = 2
n_cons_approx = 2
matrix_min_approx = create_matrix(n_var_approx, n_cons_approx)
constrain(matrix_min_approx, '1,1,L,6')
constrain(matrix_min_approx, '-1,2,L,8')
obj(matrix_min_approx, '-1,-3,0')
problem_approx = minz(matrix_min_approx)
min_approx = problem_approx['min']
problem_approx.pop('min')
coeff_approx = np.array(list(problem_approx.values()))
assert min_approx == approx(min_obj)
assert coeff_obj == approx(coeff_approx)
def solve(self):
"""
Solve the minimization problem.
"""
solve = minz(self.matrix)
self.min = solve['min']
self.coeff = np.array(list(solve.values()))[:-1]
def test_net_problem():
"""
This function will test a minimum cost network flow problem
"""
# Definimos y resolvemos problema con scipy
c_net_obj = [2, 4, 9, 3, 1, 3, 2]
A_ub_net_obj = [[1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0]]
b_ub_net_obj = [10, 80]
A_eq_net_obj = [[1, 1, 1, 0, 0, 0, 0], [-1, 0, 0, 1, 0, 0, 0],
[0, -1, 0, -1, 1, 0, 0], [0, 0, -1, 0, 0, 1, -1],
[0, 0, 0, 0, -1, -1, 1]]
b_eq_net_obj = [50, 40, 0, -30, -60]
net_obj = linprog(c_net_obj,
A_ub=A_ub_net_obj,
b_ub=b_ub_net_obj,
A_eq=A_eq_net_obj,
b_eq=b_eq_net_obj).fun
net_coeff_obj = linprog(c_net_obj,
A_ub=A_ub_net_obj,
b_ub=b_ub_net_obj,
A_eq=A_eq_net_obj,
b_eq=b_eq_net_obj).x
# Definimos y resolvemos problema con nuestro paquete
n_var_approx = 7
n_cons_approx = 7
matrix_net_approx = create_matrix(n_var_approx, n_cons_approx)
constrain(matrix_net_approx, '1,1,1,0,0,0,0,E,50')
constrain(matrix_net_approx, '-1,0,0,1,0,0,0,E,40')
constrain(matrix_net_approx, '0,-1,0,-1,1,0,0,E,0')
constrain(matrix_net_approx, '0,0,-1,0,0,1,-1,E,-30')
constrain(matrix_net_approx, '0,0,0,0,-1,-1,1,E,-60')
constrain(matrix_net_approx, '1,0,0,0,0,0,0,L,10')
constrain(matrix_net_approx, '0,0,0,0,1,0,0,L,80')
obj(matrix_net_approx, '2,4,9,3,1,3,2,0')
problem_approx = minz(matrix_net_approx)
net_approx = problem_approx['min']
problem_approx.pop('min')
net_coeff_approx = np.array(list(problem_approx.values()))
assert net_obj == approx(net_approx)
assert np.round(net_coeff_obj, 0) == approx(net_coeff_approx)
def solve(self):
solve = minz(self.matrix)
self.min = solve['min']
self.coeff = np.array(list(solve.values()))[:-1]
import numpy as np from scipy.optimize import linprog from pytest import approx from mex.simplex.simplex_networks import create_matrix, pivots_col, pivots_row, find_negative_col, find_negative_row, find_pivot_col, find_pivot_row, pivot from mex.simplex.problem_definition import add_cons, constrain, add_obj, obj, maxz, minz from memory_profiler import profile from guppy import hpy hp = hpy() hp.setrelheap() matrix_max_approx_1 = create_matrix(7, 7) constrain(matrix_max_approx_1, '1,1,1,0,0,0,0,E,50') constrain(matrix_max_approx_1, '-1,0,0,1,0,0,0,E,40') constrain(matrix_max_approx_1, '0,-1,0,-1,1,0,0,E,0') constrain(matrix_max_approx_1, '0,0,-1,0,0,1,-1,E,-30') constrain(matrix_max_approx_1, '0,0,0,0,-1,-1,1,E,-60') constrain(matrix_max_approx_1, '1,0,0,0,0,0,0,L,10') constrain(matrix_max_approx_1, '0,0,0,0,1,0,0,L,80') obj(matrix_max_approx_1, '2,4,9,3,1,3,2,0') problem_approx_1 = minz(matrix_max_approx_1) h = hp.heap() print(h)