An optimal choice Dai-Liao conjugate gradient algorithm for unconstrained optimization and portfolio selection

Jamilu Sabi’u; Ibrahim Mohammed Sulaiman; P. Kaelo; Maulana Malik; Saadi Ahmad Kamaruddin

doi:10.3934/math.2024034

An optimal choice Dai-Liao conjugate gradient algorithm for unconstrained optimization and portfolio selection

Jamilu Sabi’u, Ibrahim Mohammed Sulaiman, P. Kaelo, Maulana Malik, Saadi Ahmad Kamaruddin

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

In this research, we propose an optimal choice for the non-negative constant in the Dai-Liao conjugate gradient formula based on the prominent Barzilai-Borwein approach by leveraging the nice features of the Frobenius matrix norm. The global convergence of the new modification is demonstrated using some basic assumptions. Numerical comparisons with similar algorithms show that the new approach is reliable in terms of the number of iterations, computing time, and function evaluations for unconstrained minimization, portfolio selection and image restoration problems.

Original language	English
Pages (from-to)	642-664
Number of pages	23
Journal	AIMS Mathematics
Volume	9
Issue number	1
DOIs	https://doi.org/10.3934/math.2024034
Publication status	Published - 2024

Keywords

BB approach
descent property
global convergence Mathematics Subject Classification: 90C26, 90C30
unconstrained optimization

Access to Document

10.3934/math.2024034

Cite this

@article{316d82f601b9496782ddb973b1bf9b74,

title = "An optimal choice Dai-Liao conjugate gradient algorithm for unconstrained optimization and portfolio selection",

abstract = "In this research, we propose an optimal choice for the non-negative constant in the Dai-Liao conjugate gradient formula based on the prominent Barzilai-Borwein approach by leveraging the nice features of the Frobenius matrix norm. The global convergence of the new modification is demonstrated using some basic assumptions. Numerical comparisons with similar algorithms show that the new approach is reliable in terms of the number of iterations, computing time, and function evaluations for unconstrained minimization, portfolio selection and image restoration problems.",

keywords = "BB approach, descent property, global convergence Mathematics Subject Classification: 90C26, 90C30, unconstrained optimization",

author = "Jamilu Sabi{\textquoteright}u and Sulaiman, {Ibrahim Mohammed} and P. Kaelo and Maulana Malik and Kamaruddin, {Saadi Ahmad}",

note = "Publisher Copyright: {\textcopyright} 2024 the Author(s), licensee AIMS Press.",

year = "2024",

doi = "10.3934/math.2024034",

language = "English",

volume = "9",

pages = "642--664",

journal = "AIMS Mathematics",

issn = "2473-6988",

publisher = "AIMS Press",

number = "1",

}

TY - JOUR

T1 - An optimal choice Dai-Liao conjugate gradient algorithm for unconstrained optimization and portfolio selection

AU - Sabi’u, Jamilu

AU - Sulaiman, Ibrahim Mohammed

AU - Kaelo, P.

AU - Malik, Maulana

AU - Kamaruddin, Saadi Ahmad

PY - 2024

Y1 - 2024

N2 - In this research, we propose an optimal choice for the non-negative constant in the Dai-Liao conjugate gradient formula based on the prominent Barzilai-Borwein approach by leveraging the nice features of the Frobenius matrix norm. The global convergence of the new modification is demonstrated using some basic assumptions. Numerical comparisons with similar algorithms show that the new approach is reliable in terms of the number of iterations, computing time, and function evaluations for unconstrained minimization, portfolio selection and image restoration problems.

AB - In this research, we propose an optimal choice for the non-negative constant in the Dai-Liao conjugate gradient formula based on the prominent Barzilai-Borwein approach by leveraging the nice features of the Frobenius matrix norm. The global convergence of the new modification is demonstrated using some basic assumptions. Numerical comparisons with similar algorithms show that the new approach is reliable in terms of the number of iterations, computing time, and function evaluations for unconstrained minimization, portfolio selection and image restoration problems.

KW - BB approach

KW - descent property

KW - global convergence Mathematics Subject Classification: 90C26, 90C30

KW - unconstrained optimization

UR - http://www.scopus.com/inward/record.url?scp=85178469028&partnerID=8YFLogxK

U2 - 10.3934/math.2024034

DO - 10.3934/math.2024034

M3 - Article

AN - SCOPUS:85178469028

SN - 2473-6988

VL - 9

SP - 642

EP - 664

JO - AIMS Mathematics

JF - AIMS Mathematics

IS - 1

ER -

An optimal choice Dai-Liao conjugate gradient algorithm for unconstrained optimization and portfolio selection

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this