A new CG algorithm based on a scaled memoryless BFGS update with adaptive search strategy, and its application to large-scale unconstrained optimization problems
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Publication:2043170
DOI10.1016/j.cam.2021.113670zbMath1472.90064OpenAlexW3169009826MaRDI QIDQ2043170
Wenjuan Zhao, Xiao Liang Dong, Xiang-Li Li
Publication date: 29 July 2021
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cam.2021.113670
Uses Software
Cites Work
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- Group coordinate descent algorithms for nonconvex penalized regression
- A class of adaptive dai-liao conjugate gradient methods based on the scaled memoryless BFGS update
- On optimality of the parameters of self-scaling memoryless quasi-Newton updating formulae
- Scaled conjugate gradient algorithms for unconstrained optimization
- Optimization theory and methods. Nonlinear programming
- The Dai-Liao nonlinear conjugate gradient method with optimal parameter choices
- Testing Unconstrained Optimization Software
- Global Convergence Properties of Conjugate Gradient Methods for Optimization
- Conjugate Gradient Methods with Inexact Searches
- On the Convergence of a New Conjugate Gradient Algorithm
- Numerical Optimization
- A Nonlinear Conjugate Gradient Algorithm with an Optimal Property and an Improved Wolfe Line Search
- Convergence Conditions for Ascent Methods. II: Some Corrections
- Methods of conjugate gradients for solving linear systems
- New conjugacy conditions and related nonlinear conjugate gradient methods
- Benchmarking optimization software with performance profiles.