On solving L-SR1 trust-region subproblems

DOI10.1007/s10589-016-9868-3zbMath1364.90239arXiv1506.07222OpenAlexW2963160732MaRDI QIDQ513714

Jennifer B. Erway, Roummel F. Marcia, Johannes J. Brust

Publication date: 7 March 2017

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.07222

zbMATH Keywords

large-scale unconstrained optimizationtrust-region methodslimited-memory quasi-Newton methodssymmetric rank-one update

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Quadratic programming (90C20)

Related Items

Quasi-Newton methods for machine learning: forget the past, just sample, On efficiently combining limited-memory and trust-region techniques, Stochastic Trust-Region Methods with Trust-Region Radius Depending on Probabilistic Models, A limited-memory Riemannian symmetric rank-one trust-region method with a restart strategy, Regularization of limited memory quasi-Newton methods for large-scale nonconvex minimization, Globally Convergent Multilevel Training of Deep Residual Networks, A new multipoint symmetric secant method with a dense initial matrix, Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations, A limited-memory trust-region method for nonlinear optimization with many equality constraints, On solving large-scale limited-memory quasi-Newton equations, Large-scale quasi-Newton trust-region methods with low-dimensional linear equality constraints, Large-Scale Optimization with Linear Equality Constraints Using Reduced Compact Representation

Uses Software

• LSTRS
• GQTPAR
• MSS

Cites Work

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