Updating the self-scaling symmetric rank one algorithm with limited memory for large-scale unconstrained optimization

DOI10.1023/B:COAP.0000004977.04103.FBzbMath1045.90064OpenAlexW2051154204MaRDI QIDQ1424786

Sun Linping

Publication date: 15 March 2004

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

Full work available at URL: https://doi.org/10.1023/b:coap.0000004977.04103.fb

zbMATH Keywords

unconstrained optimizationlarge scalelimited memoryquasi Newton

Mathematics Subject Classification ID

Nonlinear programming (90C30) Methods of quasi-Newton type (90C53)

Related Items (3)

A new class of supermemory gradient methods ⋮ Stochastic proximal quasi-Newton methods for non-convex composite optimization ⋮ L-Broyden methods: a generalization of the L-BFGS method to the limited-memory Broyden family

Uses Software

• L-BFGS

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