Combining and scaling descent and negative curvature directions

DOI10.1007/s10107-009-0305-6zbMath1227.49038OpenAlexW1969778961WikidataQ61987169 ScholiaQ61987169MaRDI QIDQ543412

Alberto Olivares, Catarina P. Avelino, Javier M. Moguerza, Francisco Javier Prieto

Publication date: 17 June 2011

Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10016/14884

zbMATH Keywords

nonconvex optimizationnegative curvatureline search

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical methods based on nonlinear programming (49M37)

Related Items (4)

A regularized Newton method for degenerate unconstrained optimization problems ⋮ Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points ⋮ A decoupled first/second-order steps technique for nonconvex nonlinear unconstrained optimization with improved complexity bounds ⋮ A second-order globally convergent direct-search method and its worst-case complexity

Uses Software

• CUTEr
• SifDec

Cites Work

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• A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search
• CUTEr and SifDec
• Benchmarking optimization software with performance profiles.

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