A new multipoint symmetric secant method with a dense initial matrix

DOI10.1080/10556788.2023.2167993arXiv2107.06321OpenAlexW3181458735MaRDI QIDQ6113526

Jennifer B. Erway, Mostafa Rezapour

Publication date: 9 August 2023

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2107.06321

zbMATH Keywords

large-scale optimizationnonlinear optimizationquasi-Newton methodstrust-region methods

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Methods of quasi-Newton type (90C53)

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Cites Work

• Unnamed Item
• On solving L-SR1 trust-region subproblems
• Representations of quasi-Newton matrices and their use in limited memory methods
• A limited-memory multipoint symmetric secant method for bound constrained optimization
• A dense initialization for limited-memory quasi-Newton methods
• CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization
• On efficiently combining limited-memory and trust-region techniques
• MATLAB Guide, Third Edition
• On Efficiently Computing the Eigenvalues of Limited-Memory Quasi-Newton Matrices
• Methods of the secant type for systems of equations with symmetric jacobian matrix
• Computing a Trust Region Step
• Two-Point Step Size Gradient Methods
• Updating Quasi-Newton Matrices with Limited Storage
• Computing Optimal Locally Constrained Steps
• Quasi-Newton Methods, Motivation and Theory
• Trust Region Methods
• Accuracy and Stability of Numerical Algorithms
• Algorithm 943
• A New Algorithm for Unconstrained Optimization
• Benchmarking optimization software with performance profiles.