A matrix-free trust-region Newton algorithm for convex-constrained optimization

DOI10.1007/s11590-021-01794-1zbMath1489.90136OpenAlexW3200785117MaRDI QIDQ2119752

Drew P. Kouri

Publication date: 30 March 2022

Published in: Optimization Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11590-021-01794-1

zbMATH Keywords

Newton's methodlarge-scale optimizationnonconvex optimizationtrust regionsconvex constraintsspectral projected gradient

Mathematics Subject Classification ID

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

Related Items

A proximal trust-region method for nonsmooth optimization with inexact function and gradient evaluations

Uses Software

• PARDISO
• UCI-ml
• TRON
• LIBSVM
• Amesos
• Tpetra
• StatLib Datasets Archive
• BRENT
• ROL

Cites Work

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• Monotone and nonmonotone trust-region-based algorithms for large scale unconstrained optimization problems
• New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds
• Filters in topology optimization based on Helmholtz-type differential equations
• A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds
• Global Convergence of a a of Trust-Region Methods for Nonconvex Minimization in Hilbert Space
• Inexact spectral projected gradient methods on convex sets
• Nonmonotone Spectral Projected Gradient Methods on Convex Sets
• Global Convergence of a Class of Trust Region Algorithms for Optimization Using Inexact Projections on Convex Constraints
• Newton's Method for Large Bound-Constrained Optimization Problems
• Convergence Properties of Minimization Algorithms for Convex Constraints Using a Structured Trust Region
• Topology optimization of fluids in Stokes flow
• Practical active-set Euclidian trust-region method with spectral projected gradients for bound-constrained minimization

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