My graduate studies included learning about constraint-based optimization algorithms (such as linear programming) and ...

The original version of this story appeared in Quanta Magazine. In 1939, upon arriving late to his statistics course at UC Berkeley, George Dantzig—a first-year graduate student—copied two problems ...

Introduction: In unsupervised learning, data clustering is essential. However, many current algorithms have issues like early convergence, inadequate local search capabilities, and trouble processing ...

Abstract: In this work, we extend the simplex algorithm of linear programming for finding a local minimum of a concave quadratic function subject to box constraints. In order to test the performance ...

Mr. Murphy is the junior senator from Connecticut. Kids are even more in the bag of social media companies than we think. So many of them have ceded their online autonomy so fully to their phones that ...

Solve linear optimization problems including minimization and maximization with simplex algorithm. Uses the Big M method to solve problems with larger equal constraints in Python ...

Resolves linear programming problems (LP) with the simplex algorithm showing all the intermediate steps. With a basic interface (Glade & GTK+) input and Latex (beamer) Output.

The purpose of this research paper is to introduce Easy Simplex Algorithm which is developed by author. The simplex algorithm first presented by G. B. Dantzing, is generally used for solving a Linear ...

A new variant of the Adaptive Method (AM) of Gabasov is presented, to minimize the computation time. Unlike the original method and its some variants, we need not to compute the inverse of the basic ...

Abstract: This paper proposes a linear algorithm to optimize the compensation of reactive power, harmonic distortion and unbalanced load applied to distributed electronic power processors, for example ...