add initial solutions
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@@ -152,12 +152,13 @@ def print_result(result: Result):
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def interior_point(
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C: np.array, # Vector of objective function coefficients
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A: np.array, # Matrix of constraint coefficients
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x_0: np.array, # Initial point (vector)
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b: np.array, # Vector of right-hand side values of constraints
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x_0: np.array, # Initial point (vector)
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eps: np.float64 = 0.01, # Solution accuracy
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alpha: np.float64 = 0.5, # Step coefficient
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maximizing: bool = True) -> Result: # Flag for maximization or minimization
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# Check if the method is applicable: the initial point must satisfy the constraints
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print(A)
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if (not np.all(np.dot(A, x_0) <= b) or np.any(x_0 == 0)):
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print(np.dot(A, x_0), b)
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return Result(State.INAPPLICABLE, maximize=maximizing)
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@@ -251,14 +252,14 @@ def TEST_MINIMIZE_CASE():
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print("----------------------------RUNNING_TEST_MINIMIZE_CASE----------------------------")
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C = np.array([-2, 2, -6])
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A = np.array([
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[2, 1, -2]
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[1, 2, 4]
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[2, 1, -2],
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[1, 2, 4],
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[1, -1, 2]])
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b = np.array([24, 23, 10])
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x_0 = np.array([1, 1])
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x_0 = np.array([1, 1, 1])
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eps = 0.01
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alpha = 0.5
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maximize = True
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maximize = False
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print_initial_inputs(C, A, b, x_0, eps, alpha, maximize)
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result = interior_point(C, A, b, x_0, eps, alpha, maximize)
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@@ -288,7 +289,7 @@ def TEST_WITH_SLACK_CASE():
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[-2, -1, 0, -2],
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[3, 2, 0, 1]])
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b = np.array([10, 18, 36])
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x_0 = np.array([1, 1])
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x_0 = np.array([1, 1, 1, 1])
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eps = 0.01
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alpha = 0.5
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maximize = True
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@@ -353,7 +354,7 @@ def TEST_UNSOLVABLE_CASE():
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[-1, 0, 0, 10, 0],
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[0, 1, 1, -5, 1]])
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b = np.array([-24, 6, 1, 2])
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x_0 = np.array([1, 1])
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x_0 = np.array([-2, -3, -1, -1, 1])
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eps = 0.01
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alpha = 0.5
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maximize = True
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@@ -391,5 +392,5 @@ for test in tests:
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print("----------------------------RESULTS----------------------------")
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print("Total number of tests: " << tests.size())
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print("Total number of passed tests: " << tests_passed)
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print("Total number of tests: ", tests.size())
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print("Total number of passed tests: ", tests_passed)
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