3680947894

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github

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Commit Message

Merge a6cd61918 into b40be3bdd

Pull Request Pull Request #81: Updates coveralls.io configuration

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/src/Utility/Solver_TEmatrix_Demo.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar  1 15:16:36 2022

@author: yifeiwang
"""
import copy

constraint_matrix = []
commodity_matrix_1 = [1, 1, 0, 0, 0, 0, 0]
commodity_matrix_2 = [-1, 0, 1, 1, 0, 0, 0]
commodity_matrix_3 = [0, -1, -1, 0, 1, 1, 0]
commodity_matrix_4 = [0, 0, 0, -1, -1, 0, 1]
commodity_matrix_5 = [0, 0, 0, 0, 0, -1, -1]
zeros = [0, 0, 0, 0, 0, 0, 0]

commodity_matrix = []
commodity_matrix.append(commodity_matrix_1)
commodity_matrix.append(commodity_matrix_2)
commodity_matrix.append(commodity_matrix_3)
commodity_matrix.append(commodity_matrix_4)
commodity_matrix.append(commodity_matrix_5)


for line in commodity_matrix:
    constraint_matrix.append(line + zeros + zeros)


for line in commodity_matrix:
    constraint_matrix.append(zeros + line + zeros)

for line in commodity_matrix:
    constraint_matrix.append(zeros + zeros + line)


matrix_lessthan = []
threezeros = zeros * 3

for i in range(7):
    zerosthree = copy.deepcopy(threezeros)
    matrix_lessthan.append(zerosthree)
    matrix_lessthan[i][i] = 15
    matrix_lessthan[-1][i + 7] = 5
    matrix_lessthan[-1][i + 14] = 10


bounds_equal = [1, 0, 0, -1, 0, 1, 0, 0, 0, -1, 0, 1, 0, 0, -1]
bounds_lessthan = [20, 10, 10, 20, 40, 10, 30]
bounds = bounds_equal + bounds_lessthan
cost = [1, 1, 2, 4, 8, 5, 3, 1, 1, 2, 4, 8, 5, 3, 1, 1, 2, 4, 8, 5, 3]

finalmatrix = constraint_matrix + matrix_lessthan

print(len(bounds) - len(finalmatrix))


#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar  7 13:42:31 2022

@author: yifeiwang
"""

import json

from ortools.linear_solver import pywraplp


def create_data_model():
    """Stores the data for the problem."""
    data = {}
    data["constraint_coeffs"] = finalmatrix
    data["bounds"] = bounds
    data["obj_coeffs"] = cost
    data["num_vars"] = len(finalmatrix[0])
    data["num_constraints"] = len(finalmatrix)
    for i in range(data["num_constraints"] - 7):
        # constraint_expr = [data['constraint_coeffs'][i][j] * x[j] for j in range(data['num_vars'])]
        print(data["constraint_coeffs"][i])
    print()

    return data


def main():
    data = create_data_model()
    # Create the mip solver with the SCIP backend.
    solver = pywraplp.Solver.CreateSolver("SCIP")

    x = {}
    for j in range(data["num_vars"]):
        x[j] = solver.IntVar(0, 1, "x[%i]" % j)
    print("Number of variables =", solver.NumVariables())

    # for i in range(data['num_constraints']):
    #     constraint = solver.RowConstraint(0, data['bounds'][i], '')
    #     for j in range(data['num_vars']):
    #         constraint.SetCoefficient(x[j], data['constraint_coeffs'][i][j])
    for i in range(data["num_constraints"] - 7):
        constraint_expr = [
            data["constraint_coeffs"][i][j] * x[j] for j in range(data["num_vars"])
        ]
        solver.Add(sum(constraint_expr) == data["bounds"][i])
    for i in range(data["num_constraints"] - 7, data["num_constraints"]):
        constraint_expr = [
            data["constraint_coeffs"][i][j] * x[j] for j in range(data["num_vars"])
        ]
        solver.Add(sum(constraint_expr) <= data["bounds"][i])
    print("Number of constraints =", solver.NumConstraints())
    # In Python, you can also set the constraints as follows.
    # for i in range(data['num_constraints']):
    #  constraint_expr = \
    # [data['constraint_coeffs'][i][j] * x[j] for j in range(data['num_vars'])]
    #  solver.Add(sum(constraint_expr) <= data['bounds'][i])

    objective = solver.Objective()
    for j in range(data["num_vars"]):
        objective.SetCoefficient(x[j], data["obj_coeffs"][j])
    objective.SetMinimization()
    # In Python, you can also set the objective as follows.
    # obj_expr = [data['obj_coeffs'][j] * x[j] for j in range(data['num_vars'])]
    # solver.Maximize(solver.Sum(obj_expr))

    status = solver.Solve()

    if status == pywraplp.Solver.OPTIMAL:
        print("Objective value =", solver.Objective().Value())
        for j in range(data["num_vars"]):
            print(x[j].name(), " = ", x[j].solution_value())
        print()
        print("Problem solved in %f milliseconds" % solver.wall_time())
        print("Problem solved in %d iterations" % solver.iterations())
        print("Problem solved in %d branch-and-bound nodes" % solver.nodes())
    else:
        print("The problem does not have an optimal solution.")


if __name__ == "__main__":
    main()

1	#!/usr/bin/env python3
2	# -- coding: utf-8 --
3	"""	×
4	Created on Tue Mar 1 15:16:36 2022
5
6	@author: yifeiwang
7	"""
8	import copy	×
9
10	constraint_matrix = []	×
11	commodity_matrix_1 = [1, 1, 0, 0, 0, 0, 0]	×
12	commodity_matrix_2 = [-1, 0, 1, 1, 0, 0, 0]	×
13	commodity_matrix_3 = [0, -1, -1, 0, 1, 1, 0]	×
14	commodity_matrix_4 = [0, 0, 0, -1, -1, 0, 1]	×
15	commodity_matrix_5 = [0, 0, 0, 0, 0, -1, -1]	×
16	zeros = [0, 0, 0, 0, 0, 0, 0]	×
17
18	commodity_matrix = []	×
19	commodity_matrix.append(commodity_matrix_1)	×
20	commodity_matrix.append(commodity_matrix_2)	×
21	commodity_matrix.append(commodity_matrix_3)	×
22	commodity_matrix.append(commodity_matrix_4)	×
23	commodity_matrix.append(commodity_matrix_5)	×
24
25
26	for line in commodity_matrix:	×
27	constraint_matrix.append(line + zeros + zeros)	×
28
29
30	for line in commodity_matrix:	×
31	constraint_matrix.append(zeros + line + zeros)	×
32
33	for line in commodity_matrix:	×
34	constraint_matrix.append(zeros + zeros + line)	×
35
36
37	matrix_lessthan = []	×
38	threezeros = zeros * 3	×
39
40	for i in range(7):	×
41	zerosthree = copy.deepcopy(threezeros)	×
42	matrix_lessthan.append(zerosthree)	×
43	matrix_lessthan[i][i] = 15	×
44	matrix_lessthan[-1][i + 7] = 5	×
45	matrix_lessthan[-1][i + 14] = 10	×
46
47
48	bounds_equal = [1, 0, 0, -1, 0, 1, 0, 0, 0, -1, 0, 1, 0, 0, -1]	×
49	bounds_lessthan = [20, 10, 10, 20, 40, 10, 30]	×
50	bounds = bounds_equal + bounds_lessthan	×
51	cost = [1, 1, 2, 4, 8, 5, 3, 1, 1, 2, 4, 8, 5, 3, 1, 1, 2, 4, 8, 5, 3]	×
52
53	finalmatrix = constraint_matrix + matrix_lessthan	×
54
55	print(len(bounds) - len(finalmatrix))	×
56
57
58	#!/usr/bin/env python3
59	# -- coding: utf-8 --
60	"""
61	Created on Mon Mar 7 13:42:31 2022
62
63	@author: yifeiwang
64	"""
65
66	import json	×
67
68	from ortools.linear_solver import pywraplp	×
69
70
71	def create_data_model():	×
72	"""Stores the data for the problem."""
73	data = {}	×
74	data["constraint_coeffs"] = finalmatrix	×
75	data["bounds"] = bounds	×
76	data["obj_coeffs"] = cost	×
77	data["num_vars"] = len(finalmatrix[0])	×
78	data["num_constraints"] = len(finalmatrix)	×
79	for i in range(data["num_constraints"] - 7):	×
80	# constraint_expr = [data['constraint_coeffs'][i][j] * x[j] for j in range(data['num_vars'])]
81	print(data["constraint_coeffs"][i])	×
82	print()	×
83
84	return data	×
85
86
87	def main():	×
88	data = create_data_model()	×
89	# Create the mip solver with the SCIP backend.
90	solver = pywraplp.Solver.CreateSolver("SCIP")	×
91
92	x = {}	×
93	for j in range(data["num_vars"]):	×
94	x[j] = solver.IntVar(0, 1, "x[%i]" % j)	×
95	print("Number of variables =", solver.NumVariables())	×
96
97	# for i in range(data['num_constraints']):
98	# constraint = solver.RowConstraint(0, data['bounds'][i], '')
99	# for j in range(data['num_vars']):
100	# constraint.SetCoefficient(x[j], data['constraint_coeffs'][i][j])
101	for i in range(data["num_constraints"] - 7):	×
102	constraint_expr = [	×
103	data["constraint_coeffs"][i][j] * x[j] for j in range(data["num_vars"])
104	]
105	solver.Add(sum(constraint_expr) == data["bounds"][i])	×
106	for i in range(data["num_constraints"] - 7, data["num_constraints"]):	×
107	constraint_expr = [	×
108	data["constraint_coeffs"][i][j] * x[j] for j in range(data["num_vars"])
109	]
110	solver.Add(sum(constraint_expr) <= data["bounds"][i])	×
111	print("Number of constraints =", solver.NumConstraints())	×
112	# In Python, you can also set the constraints as follows.
113	# for i in range(data['num_constraints']):
114	# constraint_expr = \
115	# [data['constraint_coeffs'][i][j] * x[j] for j in range(data['num_vars'])]
116	# solver.Add(sum(constraint_expr) <= data['bounds'][i])
117
118	objective = solver.Objective()	×
119	for j in range(data["num_vars"]):	×
120	objective.SetCoefficient(x[j], data["obj_coeffs"][j])	×
121	objective.SetMinimization()	×
122	# In Python, you can also set the objective as follows.
123	# obj_expr = [data['obj_coeffs'][j] * x[j] for j in range(data['num_vars'])]
124	# solver.Maximize(solver.Sum(obj_expr))
125
126	status = solver.Solve()	×
127
128	if status == pywraplp.Solver.OPTIMAL:	×
129	print("Objective value =", solver.Objective().Value())	×
130	for j in range(data["num_vars"]):	×
131	print(x[j].name(), " = ", x[j].solution_value())	×
132	print()	×
133	print("Problem solved in %f milliseconds" % solver.wall_time())	×
134	print("Problem solved in %d iterations" % solver.iterations())	×
135	print("Problem solved in %d branch-and-bound nodes" % solver.nodes())	×
136	else:
137	print("The problem does not have an optimal solution.")	×
138
139
140	if __name__ == "__main__":	×
141	main()	×

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