How to Develop a Sudoku Solver Using Mixed Integer Linear Programming

In today’s blog post, let’s dive into the fascinating realm of solving Sudoku puzzles using Mixed Integer Linear Programming (MILP). Check this previous post to see how a sudoku solver can be developed in python using dynamic programming and backtracking. Sudoku, a classic logic-based number placement puzzle, has long captivated minds around the globe with… Read more How to Develop a Sudoku Solver Using Mixed Integer Linear Programming

The use of Construction Heuristics

In this post, we’ll develop a construction heuristic to build reasonable (start) solutions for the knapsack problem. Why use Construction Heuristics? Construction heuristics offer the advantage of quickly leading to acceptable solutions. Unlike more complex optimization algorithms, they are easy to implement and require less computational power. These heuristics are particularly well-suited for handling large… Read more The use of Construction Heuristics

A Metaheuristic Approach to Solving The Knapsack Problem

In the world of Operations Research and Optimization, solving complex problems efficiently is a constant pursuit. One such problem that has intrigued researchers and practitioners alike is the Knapsack Problem. In this blog post, we’ll delve into the fascinating realm of metaheuristics and guide you through the step-by-step process of applying these technique to tackle… Read more A Metaheuristic Approach to Solving The Knapsack Problem

Performance Boost in Python: Empowering Your Scripts with Numba’s JIT Compiler

In the dynamic world of Python scripting, efficiency is key. Today, let’s delve into the transformative realm of parallelization using Numba’s JIT (Just-In-Time) compiler. Brace yourselves as we explore how this powerful tool not only compiles your code on the fly but also paves the way for parallel execution. Join us on a journey to harness the full potential of your Python scripts through the magic of Numba’s parallelization capabilities.