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

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International Journal of Optimization and Control: Theories and Applications, vol.12, no.1, pp.39-46, 2022 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.11121/ijocta.2022.1027
  • Journal Name: International Journal of Optimization and Control: Theories and Applications
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Academic Search Premier, Communication Abstracts, zbMATH, Directory of Open Access Journals, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.39-46
  • Keywords: Non-smooth optimization software, BFGS, Gradient sampling algorithm, Hybrid algorithm, GRADIENT SAMPLING ALGORITHM, ROBUSTIFICATION
  • Kayseri University Affiliated: No


© 2021 Balikesir University. All rights reserved.The aim of this study is to compare the performance of smooth and nonsmooth optimization solvers from HANSO (Hybrid Algorithm for Nonsmooth Optimization) 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 problems in comparison with the nonsmooth optimization solver from HANSO. Performance profiles using the number iterations, the number of function evaluations and the number of subgradient evaluations are used to compare solvers.