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

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

doi:10.1109/EIRCON52903.2021.9613264

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

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

Universidad Nacional Mayor de San Marcos
Universidad Tecnológica del Perú

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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 language	English
Title of host publication	Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021
ISBN (Electronic)	9781665444453
DOIs	https://doi.org/10.1109/EIRCON52903.2021.9613264
State	Published - 2021
Externally published	Yes
Event	2nd IEEE Engineering International Research Conference, EIRCON 2021 - Virtual, Lima, Peru Duration: 27 Oct 2021 → 29 Oct 2021

Publication series

Name	Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021

Conference

Conference	2nd IEEE Engineering International Research Conference, EIRCON 2021
Country/Territory	Peru
City	Virtual, Lima
Period	27/10/21 → 29/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Conjugate gradient method
Nonconvex functions
Optimization problems
Quasiconvex functions

Access to Document

10.1109/EIRCON52903.2021.9613264

Cite this

Aguilar Mendoza, H. S., Papa Quiroz, E. A., & Cano Lengua, M. A. (2021). An Overview on Conjugate Gradient Methods for Optimization, Extensions and Applications. In Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021 (Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021). https://doi.org/10.1109/EIRCON52903.2021.9613264

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

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.",

keywords = "Conjugate gradient method, Nonconvex functions, Optimization problems, Quasiconvex functions",

author = "\{Aguilar Mendoza\}, \{Hans Steven\} and \{Papa Quiroz\}, \{Erik Alex\} and \{Cano Lengua\}, \{Miguel Angel\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2nd IEEE Engineering International Research Conference, EIRCON 2021 ; Conference date: 27-10-2021 Through 29-10-2021",

year = "2021",

doi = "10.1109/EIRCON52903.2021.9613264",

language = "English",

series = "Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021",

booktitle = "Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021",

}

Aguilar Mendoza, HS, Papa Quiroz, EA & Cano Lengua, MA 2021, An Overview on Conjugate Gradient Methods for Optimization, Extensions and Applications. in Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021. Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021, 2nd IEEE Engineering International Research Conference, EIRCON 2021, Virtual, Lima, Peru, 27/10/21. https://doi.org/10.1109/EIRCON52903.2021.9613264

An Overview on Conjugate Gradient Methods for Optimization, Extensions and Applications. / Aguilar Mendoza, Hans Steven; Papa Quiroz, Erik Alex; Cano Lengua, Miguel Angel.
Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021. 2021. (Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Overview on Conjugate Gradient Methods for Optimization, Extensions and Applications

AU - Aguilar Mendoza, Hans Steven

AU - Papa Quiroz, Erik Alex

AU - Cano Lengua, Miguel Angel

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Conjugate gradient method

KW - Nonconvex functions

KW - Optimization problems

KW - Quasiconvex functions

UR - https://www.scopus.com/pages/publications/85123381082

U2 - 10.1109/EIRCON52903.2021.9613264

DO - 10.1109/EIRCON52903.2021.9613264

M3 - Conference contribution

AN - SCOPUS:85123381082

T3 - Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021

BT - Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021

T2 - 2nd IEEE Engineering International Research Conference, EIRCON 2021

Y2 - 27 October 2021 through 29 October 2021

ER -

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

Abstract

Publication series

Conference

UN SDGs

Keywords

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