puan-rspy 0.4.2

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puan-rspy

Puan's python and rust connection package.

What is it?

Puan is a collection of tools for discrete optimization modelling in various programming languages. Puan-rspy binds Puan's Rust package into a Python package. Check the link for more information about the tools inside.

Install

Using pip package manager just

pip install puan-rspy

Usage

Here we construct a problem using the propositional logic classes within puan-rspy. We construct a TheoryPy using a list of statements. Each statement has a variable and (optional) an AtLeast-proposition.

As an example, we'll create a model with only boolean variables "A","B","C","a","b","x","y". Since a StatementPy only takes an integer as id parameter, we imagine them all being 0, 1, 2, ..., 6, respectively. We want to say that if any of "a" or "b" is selected, then "x" and "y" must be selected. We do this by creating new statements connecting a new variable to these variables. First we create C = x + y -2 (which is the pr.StatementPy(2, (0,1), pr.AtLeastPy([5,6],-2, pr.SignPy.Positive))). Then we remeber from logic class that an implication statement (B -> C) is equivilant to !B or C. So for the B = "a" or "b" statement, we negate it first into not "a" and not "b" which then described as B = -a - b + 0. Last, we connec B and C into a final statement A = B + C.

import puan_rspy as pr

model = pr.TheoryPy([
    # This first statement has id=0, bounds=(0,1) and an AtLeast proposition
    # saying that the sum of value of variable "1" and variable "2" and -1 must
    # be greater or equal than zero. In other words, at least one of variable "1"
    # and variable "2" must be 1.
    pr.StatementPy(0, (0,1), pr.AtLeastPy([1,2],-1, pr.SignPy.Positive)),
    pr.StatementPy(1, (0,1), pr.AtLeastPy([3,4], 1, pr.SignPy.Negative)),
    pr.StatementPy(2, (0,1), pr.AtLeastPy([5,6],-2, pr.SignPy.Positive)),

    # These are independent variable declarations, e.i. variables that are
    # not dependent on other variables
    pr.StatementPy(3, (0,1), None),
    pr.StatementPy(4, (0,1), None),
    pr.StatementPy(5, (0,1), None),
    pr.StatementPy(6, (0,1), None),
])

# Here we use puan-rust's internal solver for solving our model with these
# five different objectives, and then print the result.
for solution, objective_value, status_code in theory.solve([{3: 1}, {4: 1}, {5: 1}, {6: 1}, {3:1, 4:1}], True):
    print(solution, objective_value, status_code)

# Which prints:
# {3: 1, 6: 0, 5: 0, 4: 0} 1 5
# {3: 0, 4: 1, 5: 0, 6: 0} 1 5
# {5: 1, 4: 0, 3: 0, 6: 0} 1 5
# {5: 0, 6: 1, 4: 0, 3: 0} 1 5
# {4: 1, 5: 1, 3: 1, 6: 1} 2 5