Abstract: AdaBoost has been successfully used in many signal processing systems for data classification. It has been observed that on highly noisy data AdaBoost leads to overfitting. In this paper, a ...

Robust linear programming (RLP) is a form of linear programming that aims to find solutions that are feasible and optimal for a range of possible scenarios, rather than a single deterministic ...

Linear programming is algorithm design's trusty sidekick, making optimization problems a breeze. From resource allocation to complex decision-making, it's the Swiss army knife of efficiency.

We present two first-order primal-dual algorithms for solving saddle point formulations of linear programs, namely FWLP (Frank-Wolfe Linear Programming) and FWLP-P. The former iteratively applies the ...

This repository contains a Python implementation of the Simplex algorithm for solving Linear Programming Problems (LPPs). The Simplex algorithm is an iterative method that optimizes a linear objective ...

View on Coursera Course Description. This course continues our data structures and algorithms specialization by focussing on the use of linear and integer programming formulations for solving ...

The developed model incorporated interval nonlinear programming (INP) and fuzzy robust programming (FRP) methods within a general optimization framework. The developed IFRNP model not only could ...

For testing the addressed algorithms firstly a generator of 10,000 random linear programming problems of dimension n with m constraints, where , is introduced in order to simulate as many as possible ...

Linear programming is a mathematical optimization technique used to find the optimal solution to a linear objective function subject to linear constraints. It is used in various real-world ...