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

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

Research output: Contribution to journalArticlepeer-review

Abstract

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 αk and the search direction dk. 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.

Original languageEnglish
JournalIEEE Access
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • conjugate gradient algorithms
  • Convergence
  • line search procedure
  • Minimization
  • motion control
  • Optimization
  • portfolio selection
  • Portfolios
  • Scientific computing
  • spectral algorithm
  • STEM
  • Technological innovation
  • unconstrained optimization models

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