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A Spectral RMIL+ Conjugate Gradient Method for Unconstrained Optimization With Applications in Portfolio Selection and Motion Control | IEEE Journals & Magazine | IEEE Xplore

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


This paper presents a SCG method for unconstrained optimization models. Preliminary numerical results are presented which show that the method is efficient and promising,...

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 par...Show More

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.
This paper presents a SCG method for unconstrained optimization models. Preliminary numerical results are presented which show that the method is efficient and promising,...
Published in: IEEE Access ( Volume: 9)
Page(s): 75398 - 75414
Date of Publication: 17 May 2021
Electronic ISSN: 2169-3536

Funding Agency:


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