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Journals / An International Journal of Optimization and Control: Theories & Applications (IJOCTA) / 2022 / Cilt: 12 - Sayı: 1

Comparative assessment of smooth and non-smooth optimization solvers in HANSO software

Ali Hakan Tor
Pages
39–46
Publication Date
2022-10-11
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Performance profiles using the number iterations, the number of function eval- uations and the number of subgradient evaluations are used to compare solvers.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to compare the performance of smooth and nonsmooth optimization solvers from HANSO (Hybrid Algorithm for Nonsmooth Opti- mization) software.
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Key Findings & Data:This assessment will demonstrate how successful is the BFGS method for solving nonsmooth optimization prob- lems in comparison with the nonsmooth optimization solver from HANSO.
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Conclusion & Impact:Performance profiles using the number iterations, the number of function eval- uations and the number of subgradient evaluations are used to compare solvers.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The aim of this study is to compare the performance of smooth and nonsmooth optimization solvers from HANSO (Hybrid Algorithm for Nonsmooth Opti- mization) software. The smooth optimization solver is the implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method and the nonsmooth optimization solver is the Hybrid Algorithm for Nonsmooth Optimization. More precisely, the nonsmooth optimization algorithm is the combination of the BFGS and the Gradient Sampling Algorithm (GSA). We use well-known collection of academic test problems for nonsmooth optimization containing both convex and nonconvex problems. The motivation for this research is the importance of the comparative assessment of smooth optimization methods for solving nonsmooth optimization problems. This assessment will demonstrate how successful is the BFGS method for solving nonsmooth optimization prob- lems in comparison with the nonsmooth optimization solver from HANSO. Performance profiles using the number iterations, the number of function eval- uations and the number of subgradient evaluations are used to compare solvers.

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Cite This Article

Tor, A. H. (2022). Comparative assessment of smooth and non-smooth optimization solvers in HANSO software. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 12 - Sayı: 1, 39–46. https://ajindex.com/articles/440523

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References (1)

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