Enhancements Self-Scaling Quasi-Newton for Unconstrained Optimization

A self-scaling for the quasi-Newton tecnique is derive by using a second_order Taylor's expansion to achieve optimal computational performance. Following this, new updating formulas for the quasi-Newton method are introduced based on the newly derived self-scaling equation. The numerical results confirm this derivation and suggest that the new method could potentially rival the BFGS method in terms of performance.

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Publication Details

Journal
Advances in Nonlinear Variational Inequalities
Published
2024-08-23
DOI
https://doi.org/10.52783/anvi.v27.974
Primary Topic
Advanced Optimization Algorithms Research
Type
article
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article

Enhancements Self-Scaling Quasi-Newton for Unconstrained Optimization

Basim A. Hassan
Advances in Nonlinear Variational Inequalities
Advanced Optimization Algorithms Research
article

Enhancements Self-Scaling Quasi-Newton for Unconstrained Optimization

Basim A. Hassan
article en

Abstract

A self-scaling for the quasi-Newton tecnique is derive by using a second_order Taylor's expansion to achieve optimal computational performance. Following this, new updating formulas for the quasi-Newton method are introduced based on the newly derived self-scaling equation. The numerical results confirm this derivation and suggest that the new method could potentially rival the BFGS method in terms of performance.

Advances in Nonlinear Variational InequalitiesVol. 27(2)
Openalex Percentile: Top 68%
Advanced Optimization Algorithms Research
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Enhancements Self-Scaling Quasi-Newton for Unconstrained Optimization — Basim A. Hassan · Advances in Nonlinear Variational Inequalities (2024) | TGRS Research Map | TGRS