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Two Improved Conjugate Gradient Methods with Application in Compressive Sensing and Motion Control

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  • Min Sun
  • Jing Liu
  • Yaru Wang

Abstract

To solve the monotone equations with convex constraints, a novel multiparameterized conjugate gradient method (MPCGM) is designed and analyzed. This kind of conjugate gradient method is derivative-free and can be viewed as a modified version of the famous Fletcher–Reeves (FR) conjugate gradient method. Under approximate conditions, we show that the proposed method has global convergence property. Furthermore, we generalize the MPCGM to solve unconstrained optimization problem and offer another novel conjugate gradient method (NCGM), which satisfies the sufficient descent property without any line search. Global convergence of the NCGM is also proved. Finally, we report some numerical results to show the efficiency of two novel methods. Specifically, their practical applications in compressive sensing and motion control of robot manipulator are also investigated.

Suggested Citation

  • Min Sun & Jing Liu & Yaru Wang, 2020. "Two Improved Conjugate Gradient Methods with Application in Compressive Sensing and Motion Control," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, May.
    • Handle: RePEc:hin:jnlmpe:9175496
    DOI: 10.1155/2020/9175496
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    Cited by:

    1. Abubakar, Auwal Bala & Kumam, Poom & Malik, Maulana & Ibrahim, Abdulkarim Hassan, 2022. "A hybrid conjugate gradient based approach for solving unconstrained optimization and motion control problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 640-657.

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