CG_DESCENT

Algorithm 851: CG_DESCENT. A conjugate gradient method with guaranteed descent Recently, a new nonlinear conjugate gradient scheme was developed which satisfies the descent condition gTkdk ≤ −7/8 ‖gk‖2 and which is globally convergent whenever the line search fulfills the Wolfe conditions. This article studies the convergence behavior of the algorithm; extensive numerical tests and comparisons with other methods for large-scale unconstrained optimization are given.

This software is also peer reviewed by journal TOMS.


References in zbMATH (referenced in 135 articles , 1 standard article )

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  1. Aminifard, Zohre; Babaie-Kafaki, Saman: Dai-Liao extensions of a descent hybrid nonlinear conjugate gradient method with application in signal processing (2022)
  2. Deepho, Jitsupa; Abubakar, Auwal Bala; Malik, Maulana; Argyros, Ioannis K.: Solving unconstrained optimization problems via hybrid CD-DY conjugate gradient methods with applications (2022)
  3. Sabi’u, J.; Shah, A.; Waziri, M. Y.: A modified Hager-Zhang conjugate gradient method with optimal choices for solving monotone nonlinear equations (2022)
  4. Waziri, Mohammed Yusuf; Ahmed, Kabiru; Halilu, Abubakar Sani: A modified PRP-type conjugate gradient projection algorithm for solving large-scale monotone nonlinear equations with convex constraint (2022)
  5. Aminifard, Zohre; Babaie-Kafaki, Saman: Analysis of the maximum magnification by the scaled memoryless DFP updating formula with application to compressive sensing (2021)
  6. Awwal, A. M.; Kumam, Poom; Mohammad, Hassan; Watthayu, Wiboonsak; Abubakar, A. B.: A Perry-type derivative-free algorithm for solving nonlinear system of equations and minimizing (\ell_1) regularized problem (2021)
  7. Ivanov, Branislav; Stanimirović, Predrag S.; Shaini, Bilall I.; Ahmad, Hijaz; Wang, Miao-Kun: A novel value for the parameter in the Dai-Liao-type conjugate gradient method (2021)
  8. Kozak, David; Becker, Stephen; Doostan, Alireza; Tenorio, Luis: A stochastic subspace approach to gradient-free optimization in high dimensions (2021)
  9. Sun, Wumei; Liu, Hongwei; Liu, Zexian: A class of accelerated subspace minimization conjugate gradient methods (2021)
  10. Sutti, Marco; Vandereycken, Bart: Riemannian multigrid line search for low-rank problems (2021)
  11. Vlček, Jan; Lukšan, Ladislav: Two limited-memory optimization methods with minimum violation of the previous secant conditions (2021)
  12. Abubakar, Auwal Bala; Kumam, Poom; Mohammad, Hassan; Awwal, Aliyu Muhammed: A Barzilai-Borwein gradient projection method for sparse signal and blurred image restoration (2020)
  13. Andrei, Neculai: New conjugate gradient algorithms based on self-scaling memoryless Broyden-Fletcher-Goldfarb-Shanno method (2020)
  14. Cao, Junyue; Wu, Jinzhao: A descent conjugate gradient algorithm for optimization problems and its applications in image restoration and compression sensing (2020)
  15. Cao, Junyue; Wu, Jinzhao: A conjugate gradient algorithm and its applications in image restoration (2020)
  16. Karasözen, Bülent; Uzunca, Murat; Küçükseyhan, Tuğba: Reduced order optimal control of the convective FitzHugh-Nagumo equations (2020)
  17. Li, Min: A three term Polak-Ribière-Polyak conjugate gradient method close to the memoryless BFGS quasi-Newton method (2020)
  18. Liu, Zexian; Liu, Hongwei; Dai, Yu-Hong: An improved Dai-Kou conjugate gradient algorithm for unconstrained optimization (2020)
  19. Sabi’u, Jamilu; Shah, Abdullah; Waziri, Mohammed Yusuf: Two optimal Hager-Zhang conjugate gradient methods for solving monotone nonlinear equations (2020)
  20. Stanimirović, Predrag S.; Ivanov, Branislav; Ma, Haifeng; Mosić, Dijana: A survey of gradient methods for solving nonlinear optimization (2020)

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