![]() There is a long and rich history of the theoretical development of robust and efficient solvers for optimization problems. ![]() There are many commercial optimizer tools, but having hands-on experience with a programmatic way of doing optimization is invaluable. Knowledge of such optimization techniques is extremely useful for data scientists and machine learning (ML) practitioners as discrete and continuous optimization lie at the heart of modern ML and AI systems as well as data-driven business analytics processes. Linear and (mixed) integer programming are techniques to solve problems which can be formulated within the framework of discrete optimization. It is quite ubiquitous in as diverse applications such as financial investment, diet planning, manufacturing processes, and player or schedule selection for professional sports. IntroductionÄiscrete optimization is a branch of optimization methodology which deals with discrete quantities i.e. We will discuss how to tackle such problems using Python library PuLP and get a fast and robust solution. ![]() ![]() Linear and integer programming are key techniques for discrete optimization problems and they pop up pretty much everywhere in modern business and technology sectors. ![]()
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