glpk

from glpk import LP, Var, N, C, GLPK # Create a new problem lp = LP() # Define variables x1 = lp.add_var(name='x1', lb=0.0, ub=N, kind=C) # lb=0, ub=infinity, continuous x2 = lp.add_var(name='x2', lb=0.0, ub=N, kind=C) # Set objective function (maximize 10*x1 + 6*x2) lp.obj.dir = LP.MAX lp.obj += 10 * x1 + 6 * x2 # Add constraints # Constraint 1: x1 + x2 <= 100 c1 = lp.add_row(name='c1') c1.add_term(x1, 1.0) c1.add_term(x2, 1.0) c1.upper_bound = 100.0 # Constraint 2: 7*x1 + 3*x2 <= 350 c2 = lp.add_row(name='c2') c2.add_term(x1, 7.0) c2.add_term(x2, 3.0) c2.upper_bound = 350.0 # Constraint 3: 3*x1 + 5*x2 <= 150 c3 = lp.add_row(name='c3') c3.add_term(x1, 3.0) c3.add_term(x2, 5.0) c3.upper_bound = 150.0 # Solve the problem lp.simplex() # Print results if lp.status == GLPK.LP_OPT: print(f"Status: Optimal") print(f"Objective value: {lp.obj.value:.2f}") print(f"x1 = {x1.value:.2f}") print(f"x2 = {x2.value:.2f}") elif lp.status == GLPK.LP_FEAS: print(f"Status: Feasible, but not necessarily optimal") print(f"Objective value: {lp.obj.value:.2f}") print(f"x1 = {x1.value:.2f}") print(f"x2 = {x2.value:.2f}") else: print(f"Status: Solver terminated with status code: {lp.status}") # Clean up lp.delete()