flopt 0.4

A python Non-Linear Programming API with Heuristic approach

flopt

A python Non-Linear Programming API with Heuristic approach.

docs | tutorial

Install

PyPI

pip install flopt

GitHub

git clone https://github.com/flab-coder/flopt.git

Formulatable problems in flopt

• Linear problem

• Non-Linear problem

  minimize  2*(3*a+b)*c**2 + 3
  s.t       a + b * c <= 3
            0 <= a <= 1
            1 <= b <= 2
                 c <= 3
  

• BlackBox problem

  minimize  simulator(a, b, c)
  s.t       0 <= a <= 1
            1 <= b <= 2
            1 <= c <= 3
  

• Finding the best permutation problem ( including TSP)

• Satisfiability problem (including MAX-SAT)

Heuristic Algorithms

• Random Search
• 2-Opt
• Swarm Intelligence Search
• Other applications

Simple Example

You can write codes like PuLP application.

from flopt import Variable, Problem, Solver

# Variables
a = Variable('a', lowBound=0, upBound=1, cat='Integer')
b = Variable('b', lowBound=1, upBound=2, cat='Continuous')
c = Variable('c', upBound=3, cat='Continuous')

# Problem
prob = Problem()
prob += 2*(3*a+b)*c**2+3   # set the objective function
prob += a + b*c <= 3       # set the constraint

# Solver
solver = Solver(algo='ScipySearch')  # select the heuristic algorithm
solver.setParams(n_trial=1000)  # setting of the hyper parameters
prob.solve(solver, msg=True)    # run solver to solve the problem

# display the result, incumbent solution
print('obj value', prob.getObjectiveValue())
print('a', a.value())
print('b', b.value())
print('c', c.value())

In addition, you can represent any objective function by CustomExpression

from flopt import Variable, Problem, CustomExpression

# Variables
a = Variable('a', lowBound=0, upBound=1, cat='Integer')
b = Variable('b', lowBound=1, upBound=2, cat='Continuous')
c = Variable('c', upBound=3, cat='Continuous')

from math import sin, cos
def user_func(a, b, c):
    return (0.7*a + 0.3*cos(b)**2 + 0.1*sin(c))*abs(c)

custom_obj = CustomExpression(func=user_func, variables=[a, b, c])

prob = Problem(name='CustomExpression')
prob += custom_obj

In the case you solve TSP, Permutation Variable is useful.

from flopt import Variable, Problem, 

# Variables
perm = Variable('perm', lowBound=0, upBound=N-1, cat='Permutation')

# Object
def tsp_dist(perm):
    distance = 0
    for head, tail in zip(perm, perm[1:]+[perm[0]]):
        distance += D[head][tail]  # D is the distance matrix
    return distance
tsp_obj = CustomExpression(func=tsp_dist, variables=[perm])

# Problem
prob = Problem(name='TSP')
prob += tsp_obj