A Spectral RMIL plus Conjugate Gradient Method for Unconstrained Optimization With Applications in Portfolio Selection and Motion Control

作     者：Awwal, Aliyu Muhammed Sulaiman, Ibrahim Mohammed Malik, Maulana Mamat, Mustafa Kumam, Poom Sitthithakerngkiet, Kanokwan 

作者机构：King Mongkuts Univ Technol Thonburi KMUTT Ctr Excellence Theoret & Computat Sci TaCS CoE Fac Sci Bangkok 10140 Thailand Gombe State Univ Dept Math Fac Sci Gombe 760214 Nigeria Univ Sultan Zainal Abidin UniSZA Fac Informat & Comp Kuala Terengganu 22200 Malaysia Univ Indonesia UI Fac Math & Nat Sci Dept Math Depok 16424 Indonesia Univ Malaysia Perlis Inst Engn Math Arau 02600 Malaysia China Med Univ China Med Univ Hosp Dept Med Res Taichung 40402 Taiwan King Mongkuts Univ Technol North Bangkok KMUTNB Fac Appl Sci Dept Math Intelligent & Nonlinear Dynam Innovat Res Ctr Bangkok 10800 Thailand 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2021年第9卷

页      面：75398-75414页

核心收录：

基　　金：Center of Excellence in Theoretical and Computational Science (TaCS-CoE), King Mongkut's University of Technology Thonburi (KMUTT) Center through the Computational and Applied Science for Smart Innovation Research Cluster (CLASSIC), Faculty of Science, KMUTT Thailand Science Research and Innovation (TSRI) Basic Research Fund Fiscal year 2021 [64A306000005] Thailand Science Research and Innovation Fund King Mongkut's University of Technology North Bangkok [KMUTNB-BasicR-64-22] 

主　　题：Spectral algorithm conjugate gradient algorithms unconstrained optimization models motion control line search procedure portfolio selection 

摘      要：The Spectral conjugate gradient (SCG) methods are among the efficient variants of CG algorithms which are obtained by combining the spectral gradient parameter and CG parameter. The success of SCG methods relies on effective choices of the step-size alpha(k) and the search direction d(k). This paper presents an SCG method for unconstrained optimization models. The search directions generated by the new method possess sufficient descent property without the restart condition and independent of the line search procedure used. The global convergence of the new method is proved under the weak Wolfe line search. Preliminary numerical results are presented which show that the method is efficient and promising, particularly for large-scale problems. Also, the method was applied to solve the robotic motion control problem and portfolio selection problem.