Authors :
Vivekananda Gogi; Kruthi P.R; Shivani Deshpande
Volume/Issue :
Volume 7 - 2022, Issue 10 - October
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3DZAJz4
DOI :
https://doi.org/10.5281/zenodo.7313779
Abstract :
- Linear programming (LP) is one of the wellresearched and simple way to obtain optimized solutions
to problems. A variety of Computer Science problems
use linear programming to find the optimal solutions if
they exist and Machine Learning problems are one of
them.
Machine Learning a branch of data Science in
Computer Science. This paper present two case studies
namely Linear Programming in Reinforcement
Learning and Wasserstein barycenter to review the
synergy between Linear programming and complex
problems of Machine Learning.
Keywords :
Linear Programming, Optimisation, Machine Leaning.
- Linear programming (LP) is one of the wellresearched and simple way to obtain optimized solutions
to problems. A variety of Computer Science problems
use linear programming to find the optimal solutions if
they exist and Machine Learning problems are one of
them.
Machine Learning a branch of data Science in
Computer Science. This paper present two case studies
namely Linear Programming in Reinforcement
Learning and Wasserstein barycenter to review the
synergy between Linear programming and complex
problems of Machine Learning.
Keywords :
Linear Programming, Optimisation, Machine Leaning.
