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

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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665444453
DOI
EstadoPublicada - 2021
Evento2nd IEEE Engineering International Research Conference, EIRCON 2021 - Virtual, Lima, Perú
Duración: 27 oct. 202129 oct. 2021

Serie de la publicación

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

Conferencia

Conferencia2nd IEEE Engineering International Research Conference, EIRCON 2021
País/TerritorioPerú
CiudadVirtual, Lima
Período27/10/2129/10/21

Nota bibliográfica

Publisher Copyright:
© 2021 IEEE.

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