Add linear programming implementation using the simplex method

Author: MohdFuzailHaider

my_script.py

@@ -0,0 +1,120 @@
+import numpy as np
+
+class Tableau:
+    def __init__(self, tableau):
+        self.tableau = tableau
+        self.num_rows, self.num_cols = tableau.shape
+    
+    def pivot(self, row, col):
+        # Divide the pivot row by the pivot element
+        self.tableau[row] /= self.tableau[row, col]
+        
+        # Subtract multiples of the pivot row from all other rows
+        for i in range(self.num_rows):
+            if i != row:
+                self.tableau[i] -= self.tableau[i, col] * self.tableau[row]
+
+    def find_pivot_column(self):
+        # The pivot column is the most negative value in the objective row (row 0)
+        obj_row = self.tableau[0, :-1]
+        pivot_col = np.argmin(obj_row)
+        if obj_row[pivot_col] >= 0:
+            return -1  # No negative value, we are done
+        return pivot_col
+
+    def find_pivot_row(self, pivot_col):
+        # Calculate the ratios of the right-hand side to the pivot column entries
+        rhs = self.tableau[1:, -1]
+        col = self.tableau[1:, pivot_col]
+        ratios = []
+        for i in range(len(rhs)):
+            if col[i] > 0:
+                ratios.append(rhs[i] / col[i])
+            else:
+                ratios.append(np.inf)  # Ignore non-positive entries
+        
+        pivot_row = np.argmin(ratios) + 1  # Add 1 because we ignored row 0
+        if np.isinf(ratios[pivot_row - 1]):
+            return -1  # No valid pivot row (unbounded)
+        return pivot_row
+
+    def run_simplex(self):
+        while True:
+            pivot_col = self.find_pivot_column()
+            if pivot_col == -1:
+                break  # Optimal solution found
+
+            pivot_row = self.find_pivot_row(pivot_col)
+            if pivot_row == -1:
+                raise ValueError("Linear program is unbounded.")
+            
+            self.pivot(pivot_row, pivot_col)
+
+        return self.extract_solution()
+
+    def extract_solution(self):
+        solution = np.zeros(self.num_cols - 1)
+        for i in range(1, self.num_rows):
+            col = self.tableau[i, :-1]
+            if np.count_nonzero(col) == 1:
+                solution[np.argmax(col)] = self.tableau[i, -1]
+        return solution, -self.tableau[0, -1]  # Return solution and optimal value
+
+def construct_tableau(objective, constraints, rhs):
+    """
+    Constructs the initial tableau for the simplex algorithm.
+    
+    Parameters:
+    - objective: List of coefficients of the objective function (maximize).
+    - constraints: List of lists representing coefficients of the constraints.
+    - rhs: List of right-hand-side values of constraints.
+    
+    Returns:
+    - tableau: A numpy array representing the initial simplex tableau.
+    """
+    n_constraints = len(constraints)
+    n_vars = len(objective)
+    
+    # Creating the tableau matrix
+    tableau = np.zeros((n_constraints + 1, n_vars + n_constraints + 1))
+    
+    # Fill the objective function (row 0, cols 0 to n_vars)
+    tableau[0, :n_vars] = -np.array(objective)  # Maximization -> negate
+    
+    # Fill the constraints
+    for i in range(n_constraints):
+        tableau[i + 1, :n_vars] = constraints[i]
+        tableau[i + 1, n_vars + i] = 1  # Slack variable
+        tableau[i + 1, -1] = rhs[i]  # RHS of the constraints
+    
+    return tableau
+
+def solve_linear_program(objective, constraints, rhs):
+    # Constructing the tableau
+    tableau = construct_tableau(objective, constraints, rhs)
+    
+    # Instantiate the Tableau class
+    simplex_tableau = Tableau(tableau)
+    
+    # Run the simplex algorithm
+    solution, optimal_value = simplex_tableau.run_simplex()
+    
+    # Output the solution
+    print("Optimal Solution:", solution)
+    print("Optimal Value:", optimal_value)
+
+# Example usage
+if __name__ == "__main__":
+    # Coefficients of the objective function: maximize 3x1 + 2x2
+    objective = [3, 2]
+    
+    # Coefficients of the constraints:
+    # 2x1 + x2 <= 18
+    # x1 + 2x2 <= 20
+    constraints = [[2, 1], [1, 2]]
+    
+    # Right-hand side of the constraints
+    rhs = [18, 20]
+    
+    # Solve the linear program
+    solve_linear_program(objective, constraints, rhs)