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update output

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This commit is contained in:
Ilya Grigorev
2024-11-02 21:14:48 +05:00
parent 6468387814
commit 3472cda172
1 changed files with 52 additions and 31 deletions
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main.py
+52 -31
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@@ -28,8 +28,8 @@ class Result:
def print_initial_inputs( def print_initial_inputs(
C: np.array, # Vector of objective function coefficients C: np.array, # Vector of objective function coefficients
A: np.array, # Matrix of constraint coefficients A: np.array, # Matrix of constraint coefficients
x_0: np.array, # Initial point (vector)
b: np.array, # Vector of right-hand side values of constraints b: np.array, # Vector of right-hand side values of constraints
x_0: np.array, # Initial point (vector)
eps: np.float64 = 0.01, # Solution accuracy eps: np.float64 = 0.01, # Solution accuracy
alpha: np.float64 = 0.5, # Step coefficient alpha: np.float64 = 0.5, # Step coefficient
maximize: bool = True): maximize: bool = True):
@@ -49,11 +49,11 @@ def print_initial_inputs(
z_str = "z = " z_str = "z = "
previousIsZero = True previousIsZero = True
lastNonZero = False lastNonZero = False
for i in range(len(C)): for i in range(C.shape[0]):
isNegative = False isNegative = False
for k in range(len(C)): for k in range(C.shape[0]):
if (C[k] == 0): if (C[k] == 0):
@@ -82,13 +82,13 @@ def print_initial_inputs(
print(z_str) print(z_str)
print("\nsubject to the constrains:\n") print("\nsubject to the constrains:\n")
for i in range(len(b)): for i in range(b.shape[0]):
c_str = "" c_str = ""
previousIsZero = True previousIsZero = True
lastNonZero = False lastNonZero = False
for j in range(len(A[i])): for j in range(A.shape[1]):
isNegative = False isNegative = False
for k in range(j, len(A[i])): for k in range(j, A.shape[1]):
if (A[i][k] == 0): if (A[i][k] == 0):
@@ -154,11 +154,11 @@ def interior_point(
A: np.array, # Matrix of constraint coefficients A: np.array, # Matrix of constraint coefficients
b: np.array, # Vector of right-hand side values of constraints b: np.array, # Vector of right-hand side values of constraints
x_0: np.array, # Initial point (vector) x_0: np.array, # Initial point (vector)
eps: np.float64 = 0.01, # Solution accuracy eps: np.float64 = 1e-6, # Solution accuracy
alpha: np.float64 = 0.5, # Step coefficient alpha: np.float64 = 0.5, # Step coefficient
maximizing: bool = True) -> Result: # Flag for maximization or minimization maximizing: bool = True) -> Result: # Flag for maximization or minimization
# Check if the method is applicable: the initial point must satisfy the constraints # Check if the method is applicable: the initial point must satisfy the constraints
if (not np.all(np.dot(A, x_0) <= b) or np.any(x_0 == 0)): if (not np.all(np.dot(A, x_0) <= b) or np.any(x_0 <= 0)):
return Result(State.INAPPLICABLE, maximize=maximizing) return Result(State.INAPPLICABLE, maximize=maximizing)
# If the problem is a minimization, invert the coefficients of the objective function # If the problem is a minimization, invert the coefficients of the objective function
if (not maximizing): if (not maximizing):
@@ -201,7 +201,9 @@ def interior_point(
# Check the stopping criterion based on accuracy # Check the stopping criterion based on accuracy
if np.linalg.norm(x_new - x) <= eps: if np.linalg.norm(x_new - x) <= eps:
result = np.dot(C, x) if (maximizing) else -np.dot(C, x) result = np.dot(C, x) if (maximizing) else -np.dot(C, x)
return Result(State.SOLVED, objective_function_value=result, solution=x, maximize=maximizing) return Result(
State.SOLVED, objective_function_value=np.round(result, 3), solution=np.round(x, 3), maximize=maximizing
)
iteration += 1 iteration += 1
@@ -224,7 +226,7 @@ def TEST_CASE_GENERAL_A05():
[0, 1]]) [0, 1]])
b = np.array([24, 6, 1, 2]) b = np.array([24, 6, 1, 2])
x_0 = np.array([1, 1]) x_0 = np.array([1, 1])
eps = 0.01 eps = 1e-4
alpha = 0.5 alpha = 0.5
maximize = True maximize = True
@@ -257,7 +259,7 @@ def TEST_CASE_GENERAL_A09():
[0, 1]]) [0, 1]])
b = np.array([24, 6, 1, 2]) b = np.array([24, 6, 1, 2])
x_0 = np.array([1, 1]) x_0 = np.array([1, 1])
eps = 0.01 eps = 1e-4
alpha = 0.9 alpha = 0.9
maximize = True maximize = True
@@ -288,7 +290,7 @@ def TEST_MINIMIZE_CASE_A05():
[1, -1, 2]]) [1, -1, 2]])
b = np.array([24, 23, 10]) b = np.array([24, 23, 10])
x_0 = np.array([1, 1, 1]) x_0 = np.array([1, 1, 1])
eps = 0.01 eps = 1e-4
alpha = 0.5 alpha = 0.5
maximize = False maximize = False
@@ -320,7 +322,7 @@ def TEST_MINIMIZE_CASE_A09():
[1, -1, 2]]) [1, -1, 2]])
b = np.array([24, 23, 10]) b = np.array([24, 23, 10])
x_0 = np.array([1, 1, 1]) x_0 = np.array([1, 1, 1])
eps = 0.01 eps = 1e-4
alpha = 0.9 alpha = 0.9
maximize = True maximize = True
@@ -353,7 +355,7 @@ def TEST_WITH_SLACK_CASE_A05():
[3, 2, 0, 1]]) [3, 2, 0, 1]])
b = np.array([10, 18, 36]) b = np.array([10, 18, 36])
x_0 = np.array([1, 1, 1, 1]) x_0 = np.array([1, 1, 1, 1])
eps = 0.01 eps = 1e-4
alpha = 0.5 alpha = 0.5
maximize = True maximize = True
@@ -385,7 +387,7 @@ def TEST_WITH_SLACK_CASE_A09():
[3, 2, 0, 1]]) [3, 2, 0, 1]])
b = np.array([10, 18, 36]) b = np.array([10, 18, 36])
x_0 = np.array([1, 1, 1, 1]) x_0 = np.array([1, 1, 1, 1])
eps = 0.01 eps = 1e-4
alpha = 0.9 alpha = 0.9
maximize = True maximize = True
@@ -416,14 +418,14 @@ def TEST_UNBOUNDED_CASE_A05():
[2, 0]]) [2, 0]])
b = np.array([10, 40]) b = np.array([10, 40])
x_0 = np.array([1, 1]) x_0 = np.array([1, 1])
eps = 0.01 eps = 1e-4
alpha = 0.5 alpha = 0.5
maximize = True maximize = True
print_initial_inputs(C, A, b, x_0, eps, alpha, maximize) print_initial_inputs(C, A, b, x_0, eps, alpha, maximize)
result = interior_point(C, A, b, x_0, eps, alpha, maximize) result = interior_point(C, A, b, x_0, eps, alpha, maximize)
expected_state = State.SOLVED expected_state = State.UNSOLVED
if result.state == expected_state: if result.state == expected_state:
print_result(result) print_result(result)
return 1 return 1
@@ -448,14 +450,14 @@ def TEST_UNBOUNDED_CASE_A09():
[2, 0]]) [2, 0]])
b = np.array([10, 40]) b = np.array([10, 40])
x_0 = np.array([1, 1]) x_0 = np.array([1, 1])
eps = 0.01 eps = 1e-4
alpha = 0.9 alpha = 0.9
maximize = True maximize = True
print_initial_inputs(C, A, b, x_0, eps, alpha, maximize) print_initial_inputs(C, A, b, x_0, eps, alpha, maximize)
result = interior_point(C, A, b, x_0, eps, alpha, maximize) result = interior_point(C, A, b, x_0, eps, alpha, maximize)
expected_state = State.SOLVED expected_state = State.UNSOLVED
if result.state == expected_state: if result.state == expected_state:
print_result(result) print_result(result)
return 1 return 1
@@ -482,14 +484,14 @@ def TEST_UNSOLVABLE_CASE_A05():
[0, 1, 1, -5, 1]]) [0, 1, 1, -5, 1]])
b = np.array([-24, 6, 1, 2]) b = np.array([-24, 6, 1, 2])
x_0 = np.array([-2, -3, -1, -1, 1]) x_0 = np.array([-2, -3, -1, -1, 1])
eps = 0.01 eps = 1e-4
alpha = 0.5 alpha = 0.5
maximize = True maximize = True
print_initial_inputs(C, A, b, x_0, eps, alpha, maximize) print_initial_inputs(C, A, b, x_0, eps, alpha, maximize)
result = interior_point(C, A, b, x_0, eps, alpha, maximize) result = interior_point(C, A, b, x_0, eps, alpha, maximize)
expected_state = State.SOLVED expected_state = State.INAPPLICABLE
if result.state == expected_state: if result.state == expected_state:
print_result(result) print_result(result)
return 1 return 1
@@ -517,14 +519,14 @@ def TEST_UNSOLVABLE_CASE_A09():
[0, 1, 1, -5, 1]]) [0, 1, 1, -5, 1]])
b = np.array([-24, 6, 1, 2]) b = np.array([-24, 6, 1, 2])
x_0 = np.array([-2, -3, -1, -1, 1]) x_0 = np.array([-2, -3, -1, -1, 1])
eps = 0.01 eps = 1e-4
alpha = 0.9 alpha = 0.9
maximize = True maximize = True
print_initial_inputs(C, A, b, x_0, eps, alpha, maximize) print_initial_inputs(C, A, b, x_0, eps, alpha, maximize)
result = interior_point(C, A, b, x_0, eps, alpha, maximize) result = interior_point(C, A, b, x_0, eps, alpha, maximize)
expected_state = State.SOLVED expected_state = State.INAPPLICABLE
if result.state == expected_state: if result.state == expected_state:
print_result(result) print_result(result)
return 1 return 1
@@ -541,18 +543,37 @@ def TEST_UNSOLVABLE_CASE_A09():
return 0 return 0
simplex_general_case_decVar_str = ("----------------------------SIMPLEX_TEST_GENERAL_CASE----------------------------\n"
"Decision variables: [3, 1.5]")
simplex_minimize_case_decVar_str = ("----------------------------SIMPLEX_TEST_MINIMIZE_CASE"
"----------------------------\n"
"Decision variables: [0, 0.75, 5.375]")
simplex_slack_case_decVar_str = ("----------------------------SIMPLEX_TEST_SLACK_CASE----------------------------\n"
"Decision variables: [11.5, 0.75, 0, 0]")
simplex_unbounded_case_decVar_str = ("----------------------------SIMPLEX_TEST_UNBOUNDED_CASE"
"----------------------------\n"
"Decision variables: None")
simplex_unsolvable_case_decVar_str = ("----------------------------SIMPLEX_TEST_UNSOLVABLE_CASE"
"----------------------------\n"
"Decision variables: None")
tests = [ tests = [
TEST_CASE_GENERAL_A05(), TEST_CASE_GENERAL_A09(), [TEST_CASE_GENERAL_A05(), TEST_CASE_GENERAL_A09(), simplex_general_case_decVar_str],
TEST_MINIMIZE_CASE_A05(), TEST_MINIMIZE_CASE_A09(), [TEST_MINIMIZE_CASE_A05(), TEST_MINIMIZE_CASE_A09(), simplex_minimize_case_decVar_str],
TEST_WITH_SLACK_CASE_A05(), TEST_WITH_SLACK_CASE_A09(), [TEST_WITH_SLACK_CASE_A05(), TEST_WITH_SLACK_CASE_A09(), simplex_slack_case_decVar_str],
TEST_UNBOUNDED_CASE_A05(), TEST_UNBOUNDED_CASE_A09(), [TEST_UNBOUNDED_CASE_A05(), TEST_UNBOUNDED_CASE_A09(), simplex_unbounded_case_decVar_str],
TEST_UNSOLVABLE_CASE_A05(), TEST_UNSOLVABLE_CASE_A09() [TEST_UNSOLVABLE_CASE_A05(), TEST_UNSOLVABLE_CASE_A09(), simplex_unsolvable_case_decVar_str]
] ]
tests_passed = 0 tests_passed = 0
for test in tests: for test in tests:
tests_passed += test for test_variant_i in range(len(test)):
if (test_variant_i == 2):
print(test[2])
else:
tests_passed += test[test_variant_i]
print("----------------------------RESULTS----------------------------") print("----------------------------RESULTS----------------------------")
print("Total number of tests: ", tests.size()) print(f"Total number of tests: {len(tests) * 2}")
print("Total number of passed tests: ", tests_passed) print(f"Total number of passed tests: {tests_passed}")