Several accelerated subspace minimization conjugate gradient methods based on regularization model and convergence rate analysis for nonconvex problems

DOI10.1007/s11075-022-01319-6OpenAlexW4280609174MaRDI QIDQ2098802

Yanyan Li

Publication date: 22 November 2022

Published in: Numerical Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11075-022-01319-6

zbMATH Keywords

convergenceconjugate gradient methodKurdyka-Łojasiewicz propertysubspace minimizationregularization model

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30) Methods of reduced gradient type (90C52)

Uses Software

• L-BFGS
• CUTEr
• SifDec
• CG_DESCENT

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• A Barzilai-Borwein conjugate gradient method
• Proximal alternating linearized minimization for nonconvex and nonsmooth problems
• An accelerated subspace minimization three-term conjugate gradient algorithm for unconstrained optimization
• Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
• Adaptive cubic regularisation methods for unconstrained optimization. II: Worst-case function- and derivative-evaluation complexity
• Updating the regularization parameter in the adaptive cubic regularization algorithm
• On the limited memory BFGS method for large scale optimization
• Modified two-point stepsize gradient methods for unconstrained optimization
• A subspace conjugate gradient algorithm for large-scale unconstrained optimization
• An efficient Barzilai-Borwein conjugate gradient method for unconstrained optimization
• An efficient gradient method with approximate optimal stepsize for large-scale unconstrained optimization
• New subspace minimization conjugate gradient methods based on regularization model for unconstrained optimization
• A class of accelerated subspace minimization conjugate gradient methods
• A subspace minimization conjugate gradient method based on conic model for unconstrained optimization
• On global minimizers of quadratic functions with cubic regularization
• A new subspace minimization conjugate gradient method with nonmonotone line search for unconstrained optimization
• A Newton-like method with mixed factorizations and cubic regularization for unconstrained minimization
• On the use of iterative methods in cubic regularization for unconstrained optimization
• Convergence of a Regularized Euclidean Residual Algorithm for Nonlinear Least-Squares
• A Modified BFGS Algorithm for Unconstrained Optimization
• Algorithm 851
• Two-Point Step Size Gradient Methods
• Numerical Optimization
• Relatively Smooth Convex Optimization by First-Order Methods, and Applications
• A Nonmonotone Line Search Technique and Its Application to Unconstrained Optimization
• A Subspace Study on Conjugate Gradient Algorithms
• A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property
• A new subspace minimization conjugate gradient method based on tensor model for unconstrained optimization
• Strong local convergence properties of adaptive regularized methods for nonlinear least squares
• A Nonlinear Conjugate Gradient Algorithm with an Optimal Property and an Improved Wolfe Line Search
• A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search
• Function minimization by conjugate gradients
• The Limited Memory Conjugate Gradient Method
• CUTEr and SifDec
• The conjugate gradient method in extremal problems
• Methods of conjugate gradients for solving linear systems
• Benchmarking optimization software with performance profiles.

This page was built for publication: Several accelerated subspace minimization conjugate gradient methods based on regularization model and convergence rate analysis for nonconvex problems