An Overview on Conjugate Gradient Methods for Optimization, Extensions and Applications

Hans Steven Aguilar Mendoza, Erik Alex Papa Quiroz, Miguel Angel Cano Lengua

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper aims to identify the current state of the art of the latest research related to Conjugate Gradient (CG) methods for unconstrained optimization through a systematic literature review according to the methodology proposed by Kitchenham and Charter, to answer the following research questions: Q1: In what research areas are the conjugate gradient method used? Q2: Can Dai-Yuan conjugate gradient algorithm be effectively applied in portfolio selection? Q3: Have conjugate gradient methods been used to develop large-scale numerical results? Q4: What conjugate gradient methods have been used to minimize quasiconvex or nonconvex functions? We obtain useful results to extend the applications of the CG methods, develop efficient algorithms, and continue studying theoretical convergence results.

Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665444453
DOIs
StatePublished - 2021
Event2nd IEEE Engineering International Research Conference, EIRCON 2021 - Virtual, Lima, Peru
Duration: 27 Oct 202129 Oct 2021

Publication series

NameProceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021

Conference

Conference2nd IEEE Engineering International Research Conference, EIRCON 2021
Country/TerritoryPeru
CityVirtual, Lima
Period27/10/2129/10/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Conjugate gradient method
  • Nonconvex functions
  • Optimization problems
  • Quasiconvex functions

Fingerprint

Dive into the research topics of 'An Overview on Conjugate Gradient Methods for Optimization, Extensions and Applications'. Together they form a unique fingerprint.

Cite this