An Integer Programming problem is a problem constructed to ensure mathematical optimization or feasibility by providing that some or all of the variables involved in the issue are integers.
Suppose some decision variables in the problem are found not to be discrete. In that case, they are classified as a mixed-integer problem, more commonly known as
MIP/MILP (Mixed-Integer Linear Programming).
So in this Python article, we will explore the various methodologies and libraries we can use to solve these kinds of problems in Python.
Mixed-Integer Programming Problems in Python
A mixed-integer programming (
MIP) problem is a problem in which some decision variables are ensured to be strictly integer values for an optimal solution.
Using these integer variables broadens the horizon and score of helpful optimization problems that a programmer can use to define and solve most efficiently and accurately.
An essential scenario in
MIP is the decision variable considered binary; in other words, it can only be represented as either
These are usually referred to as Binary Integer Values. These decision variables are typically used to model
No decisions based on careful calculations.
Now there are several solvers out there that were designed to deal with these kinds of problems.
These include the state-of-the-art
Python-MIP, among the most commonly known and sought-after mixed-integer linear programming solvers.
Another highly configurable
MIP solver is the
COIN-OR Branch-&-Cut solver. Finally,
Python-MIP makes developing high-performing, MIP-based solvers for any custom application easy.
It provides high-end and modern features, described and explained in full detail below.
Python Tools for
In Python, we have a vast library called
MIP, essentially a collection of Python-based tools for modelling and solving mixed-integer linear programming problems.
With a syntax inspired greatly by
MIP provides the users access to advanced and efficient features like
solution pools, and
More of its prominent features include:
Most programmers have developed their skills in modelling using a high-level programming language as it is easy. However, we can write our
MIP modelsin Python quickly.
The operator overloading feature makes the whole process of writing linear expressions much smoother in Python.
With features like
lazy constraints, a programmer can work with solid formulations using many constraints.
It generates only the inequalities required during the
branch and cutsearch. Then, to add, you can query into the
solution poolto extract or go through the top-notch solutions found during the search.
MIPstartenables a programmer to initially utilize a problem-dependent heuristic to create feasible solutions for the
Fast & Efficient
MIPpackage in Python makes a direct
callto the native dynamic loadable library of the already installed solver using the
These models are efficiently stored and made efficient by the solver. Meanwhile,
MIPdeals with communicating with your code. While the official
GurobiPython interface also provides features to handle
MIPlibrary in Python is compatible with
It can run
25times faster than it, as its performance is based on
MIPin Python was constructed to be thoroughly integrated with the
C-based librariesof the
COIN-OR Branch-&-Cutsolver and
MIP, you don’t have to worry about writing a means for communication between different solvers, as it is dealt with in the
You need only write a single, solver-independent code.
Equipped with the Latest Python Versions
As mentioned above,
MIPis compatible with Python versions 3.6 and above, so we don’t have to worry about redundancy slowing you down.
Now you know what mixed-Integer Programming is and the different solvers available to guide you through any integer programming problem that may need solving in the most efficient and usefully optimized manner.
You can also explore the official documentation for all the solvers mentioned to find solutions for your specific problem.