Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Institution Author: search.filters.institutionAuthor.Tor, Ali HakanWoS Q: search.filters.wosQuartile.Q1

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  • Article
    Comparative Assessment of Smooth and Non-Smooth Optimization Solvers in Hanso Software
    (Ramazan Yaman, 2021-10-27) Tor, Ali Hakan
    The aim of this study is to compare the performance of smooth and nonsmooth 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 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.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    An Introduction to Non-Smooth Convex Analysis via Multiplicative Derivative
    (Taylor & Francis Ltd, 2019-02-12) Tor, Ali Hakan
    In this study, *-directional derivative and *-subgradient are defined using the multiplicative derivative, making a new contribution to non-Newtonian calculus for use in non-smooth analysis. As for directional derivative and subgradient, which are used in the non-smooth optimization theory, basic definitions and preliminary facts related to optimization theory are stated and proved, and the *-subgradient concept is illustrated by providing some examples, such as absolute value and exponential functions. In addition, necessary and sufficient optimality conditions are obtained for convex problems.