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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

3 Citas (Scopus)

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 original	Inglés
Título de la publicación alojada	Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781665444453
DOI	https://doi.org/10.1109/EIRCON52903.2021.9613264
Estado	Publicada - 2021
Publicado de forma externa	Sí
Evento	2nd IEEE Engineering International Research Conference, EIRCON 2021 - Virtual, Lima, Perú Duración: 27 oct. 2021 → 29 oct. 2021

Serie de la publicación

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

Conferencia

Conferencia	2nd IEEE Engineering International Research Conference, EIRCON 2021
País/Territorio	Perú
Ciudad	Virtual, Lima
Período	27/10/21 → 29/10/21

Nota bibliográfica

Publisher Copyright:
© 2021 IEEE.

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10.1109/EIRCON52903.2021.9613264

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

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

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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.",

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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 = "Ingl{\'e}s",

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Aguilar Mendoza, HS, Papa Quiroz, EA & Cano Lengua, MA 2021, An Overview on Conjugate Gradient Methods for Optimization, Extensions and Applications. En Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021. Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021, Institute of Electrical and Electronics Engineers Inc., 2nd IEEE Engineering International Research Conference, EIRCON 2021, Virtual, Lima, Perú, 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. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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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

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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

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Aguilar Mendoza HS, Papa Quiroz EA, Cano Lengua MA. An Overview on Conjugate Gradient Methods for Optimization, Extensions and Applications. En Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021). doi: 10.1109/EIRCON52903.2021.9613264

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

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