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Home Machine Learning

Linear Programming Optimization: The Simplex Technique | by Jarom Hulet | Sep, 2024

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September 10, 2024
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If we use AI to do our work – what’s our job, then?

10 Python One-Liners Each Machine Studying Practitioner Ought to Know


Half 3: The algorithm underneath the hood

Jarom Hulet

Towards Data Science

Photograph by Daniel Cassey Pahati on Pexels.com

Up till now, this sequence has lined the fundamentals of linear programming. On this article, we’re going to transfer from primary ideas into the main points underneath the hood! This text will cowl the simplex technique, which is the algorithm that’s usually used to resolve linear programming issues. Whereas we’ll remedy a easy linear programming instance by hand with the simplex technique, our focus can be on the instinct of the algorithm slightly than memorizing the algorithmic steps (we’ve got computer systems for that sort of stuff!).

Right here is what we will cowl:

  1. Why the simplex technique is required
  2. Transferring from graphical options to algebraic
  3. Demonstrating how the simplex technique works with a easy instance

Within the first article of this sequence, we went over how the attributes of linear programming permit it to solely contemplate the nook factors of constraints as potential optimum options. This can be a very highly effective function that narrows an infinite answer house to a finite answer house. Within the examples we reviewed, we solely had a couple of constraints and some variables — we even solved a few of them by hand! After taking a look at…

Tags: HuletJaromLinearMethodOptimizationProgrammingSepSimplex

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