Create another algorithm under Linear Programming dual_simplex.py

Dual-Simplex Algorithm using PULP
The prob.solve() function applies the Dual Simplex method indirectly by solving the dual of the primal LP problem.

Pulp doesn’t directly expose the option to select the dual simplex solver, but it will use it when necessary if you alter constraints dynamically (reoptimization).

Author: 0mehedihasan

Diff for: linear_programming/dual_simplex.py

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+# Import PuLP library
+from pulp import LpMaximize, LpProblem, LpVariable, LpStatus, value
+
+# Create a Linear Programming Minimization problem
+prob = LpProblem("Dual_Simplex_Example", LpMaximize)
+
+# Create decision variables for the dual problem
+y1 = LpVariable('y1', lowBound=0)  # y1 >= 0
+y2 = LpVariable('y2', lowBound=0)  # y2 >= 0
+y3 = LpVariable('y3', lowBound=0)  # y3 >= 0
+
+# Objective function (minimization in dual corresponds to maximization here)
+prob += 4 * y1 + 2 * y2 + 3 * y3, "Objective"
+
+# Constraints for the dual problem
+prob += y1 + y2 >= 3, "Constraint 1"
+prob += y1 + y3 >= 2, "Constraint 2"
+
+# Solve the problem using the dual simplex method
+prob.solve()
+
+# Print the status of the solution
+print(f"Status: {LpStatus[prob.status]}")
+
+# Print the results (optimal values for y1, y2, and y3)
+print(f"y1 = {value(y1)}")
+print(f"y2 = {value(y2)}")
+print(f"y3 = {value(y3)}")
+
+# Print the objective value (minimized value)
+print(f"Optimal objective value: {value(prob.objective)}")
+