def solve_ilp(dictionary, indices):
initialized = initialize(dictionary, np.copy(dictionary[-2, 1:]),
np.copy(dictionary[-1, 1:]))
if initialized is None:
return 'INFEASIBLE'
else:
solved = solve(initialized)
if solved is None:
return 'UNBOUNDED'
elif not is_ilp_final(solved, indices):
return solve_ilp(add_cutting_planes(solved), indices)
else:
return solved
def main():
new_file = open(argv[1], "r")
model, n_rows, n_cols = simplex.read_model(new_file)
c, A, b, non_basic, basic = simplex.create_model(model, n_rows, n_cols)
if (b < 0).any():
print("\nThere are negative elements in the b vector.\nI'm sorry, I can't solve this yet :-(\n")
return
z, w, xB, basic, status = simplex.solve(A, b, c, basic, non_basic)
print("obj:", z)
print("basic variables", basic, "with values:", xB)
print("status:", status)
def test(A, b, c, m, n): Aup = A[:m, :n] cup = c[0, :n] res = opt.linprog(-1 * cup, Aup, b) c = np.append(c, 0) optV, vec, num_pivots = s.solve(A, b, c, 'max', default_method, False) if not np.allclose(optV, -1 * res['fun']): print opt, vec, num_pivots print 'NOT THE SAME AS SCIPY' exit(1) return np.allclose(optV, -1 * res['fun']), optV, num_pivots
def solve_ilp(dictionary, indices):
initialized = initialize(dictionary, np.copy(dictionary[-2, 1:]), np.copy(dictionary[-1, 1:]))
if initialized is None:
return 'INFEASIBLE'
else:
solved = solve(initialized)
if solved is None:
return 'UNBOUNDED'
elif np.array_equal(dictionary, solved):
return 'INFEASIBLE'
elif not is_ilp_final(solved, indices):
return solve_ilp(add_cutting_planes(solved), indices)
else:
return solved
import simplex
from test_data import problems
from numpy import matrix
simplex.enable_formatting()
for problem in problems:
print("Problem:")
print("c =", problem["c"])
print(matrix(problem["A"]))
print("b =", problem["b"])
expected_result = problem.pop("expected_result")
result = simplex.solve(**problem)
assert all((result -
expected_result) < 0.01), f"Result should be {expected_result}"
print("Result is correct!\n\n")
from sys import argv
from graph import Graph
from simplex import solve
if __name__ == "__main__":
graph = Graph(argv[1])
# Set initial feasible solution
graph.base_size = 3
graph.set_flow(from_id=1, to_id=2, value=4)
graph.set_flow(from_id=2, to_id=3, value=6)
graph.set_flow(from_id=3, to_id=4, value=5)
solve(graph)
print(graph)
for i in ans[2]:
print(i, end=' ')
elif (len(ans) == 4):
print("ilimitada")
for i in ans[0][n:n + m]:
print(i, end=' ')
print()
for i in ans[1][n:n + m]:
print(i, end=' ')
print()
else:
firstBase = [[i, i + m] for i in range(n)]
ans = solve(A, B, C, firstBase)
if (len(ans) == 5):
print("otima")
print(ans[0])
for i in ans[1][:m]:
print(i, end=' ')
print()
for i in ans[2]:
print(i, end=' ')
elif (len(ans) == 4):
print("ilimitada")
for i in ans[0]:
print(i, end=' ')
print()
for i in ans[1]:
def process(msg, url):
try:
text = msg['message']['text']
text = text.split('\n')
print(text)
print("chat_id = {}".format(msg['message']['chat']['id']))
st = 0
minim = True
if not text[0][0].isdigit() and not text[0][0] == '-':
minim = False
st = 1
c = [int(x) for x in text[st+0].split()]
b = [int(x) for x in text[st+1].split()]
A_simple = [int(x) for x in text[st+2].split()]
A = []
elem_num = 0
for i in range(len(b)):
A.append([])
for j in range(len(c)):
A[i].append(A_simple[elem_num])
elem_num += 1
tosend = {}
tosend['chat_id'] = msg['message']['chat']['id']
task = str(c) + '\n' + str(b) + '\n' + str(A) + '\n'
tosend['text'] = task
requests.post(url + 'sendMessage', params = tosend)
tosend['text'] = simplex.solve(A,b,c, minim)
requests.post(url + 'sendMessage', params = tosend)
except:
print("Message processing failed.")
try:
tosend = {}
tosend['chat_id'] = msg['message']['chat']['id']
user_manual_string = "User Manual:\n" +\
"First line - vector с.\n" +\
"Second line - vector b.\n" +\
"Third line - matrix A (all rows in the same line).\n" +\
"If you want maximization instead of minimization, write any word before first line.\n" +\
"All information should go in one message!\n" +\
"If parsing fails you will receive this message again.\n\n" +\
"Example of maximization:\n" +\
"max\n" +\
"4 -3 1 -1\n" +\
"4 5 6\n" +\
"3 2 1 0 1 0 2 1 2 1 0 2\n\n" +\
"Example of minimization:\n" +\
"4 -3 1 -1\n" +\
"4 5 6\n" +\
"3 2 1 0 1 0 2 1 2 1 0 2\n"
tosend['text'] = user_manual_string
requests.post(url + 'sendMessage', params = tosend)
print("Sended manual for user.")
except:
print("Sending manual for user failed.")
return
A1 = np.concatenate((A1, np.eye(n), -1 * np.eye(n)), axis=1)
A2 = np.concatenate((A2, -1 * np.eye(n), np.eye(n)), axis=1)
A3 = np.ones((1, (1 + len(active_set))))
A3 = np.concatenate((A3, np.zeros((1, 2 * n))), axis=1)
A4 = -1 * A3
A = np.concatenate((A1, A2, A3, A4), axis=0)
c = np.concatenate((np.zeros((1, 1 + len(active_set))), np.ones(
(1, 2 * n))),
axis=1)
final_matrix_1 = np.concatenate((A, c), axis=0)
final_matrix_2 = np.zeros((2 * n + 3, 1))
final_matrix_2[-2] = -1
final_matrix_2[-3] = 1
A = np.concatenate((final_matrix_1, final_matrix_2), axis=1)
x, y, z = simplex.solve(A, n)
d = y[0:n] - y[n:2 * n]
print('X:', X_list[-1])
print('active_set', active_set)
print(A)
print('d', d, 'z', z)
if (abs(z) <= 1e-3):
break
lambda_max = 10
while (lambda_max >= 0):
temp = g_list(current + lambda_max * d)
if (np.sum(temp > 0) == 0):
break