dantzigs-simplex-algorithm

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.

Features

• Fast Numpy implementation
• Bland's Rule for anti-cycling
• Two-Phase Method
• Supports infeasible and unbounded problems
• Extensive documentation

Usage

Code

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)

Output

[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]

Contributing

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.