This is an Python implementation of the Simplex Algorithm used in Linear Programming. The program requires specification of the objective function, technological coefficients, and the constraints of a linear programming problem and outputs the optimal solution and objective value (if any). The documentation is available here.
- Fast Numpy implementation
- Bland's Rule for anti-cycling
- Two-Phase Method
- Supports infeasible and unbounded problems
- Extensive documentation
from simplex.solver import SimplexSolver
from test_cases import test_cases
problem = test_cases[1]
# initialize solver
solver = SimplexSolver(obj_func=problem['obj_func'],
coeffs=problem['coeffs'],
constraints=problem['constraints'])
# run solver
sol = solver.solve(use_blands_rule=False,
print_tableau=True)[0] Initial Tableau
1x 2x 3x 4x 5x RHS
z [-2. -3. 0. 0. 0. 0.]
x3 [ 1. -2. 1. 0. 0. 4.]
x4 [ 2. 1. 0. 1. 0. 18.]
x5 [ 0. 1. 0. 0. 1. 10.]
[1] Pivoted around (2, 1)
1x 2x 3x 4x 5x RHS
z [-2. 0. 0. 0. 3. 30.]
x3 [ 1. 0. 1. 0. 2. 24.]
x4 [ 2. 0. 0. 1. -1. 8.]
x2 [ 0. 1. 0. 0. 1. 10.]
[2] Pivoted around (1, 0)
1x 2x 3x 4x 5x RHS
z [ 0. 0. 0. 1. 2. 38.]
x3 [ 0. 0. 1. -.5 2.5 20.]
x1 [ 1. 0. 0. .5 -0.5 4.]
x2 [ 0. 1. 0. 0. 1. 10.]
Solution:
z*=38.0,
x*=[4.0, 10.0, 20.0]
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. Please make sure to adhere to the code style and add sufficient documentation.