Variable Metric Methods for Unconstrained Optimization and Nonlinear Least Squares

0403533 - UIVT-O 20000015 RIV NL eng J - Journal Article
Lukšan, Ladislav - Spedicato, E.
Variable Metric Methods for Unconstrained Optimization and Nonlinear Least Squares.
Journal of Computational and Applied Mathematics. Roč. 124, č. 1-2 (2000), s. 61-95. ISSN 0377-0427. E-ISSN 1879-1778
R&D Projects: GA ČR GA201/00/0080
Institutional research plan: AV0Z1030915
Keywords : quasi-Newton methods * variable metric methods * unconstrained optimization * nonlinear least squares * sparse problems * partially separable problems * limited-memory methods
Subject RIV: BA - General Mathematics
Impact factor: 0.455, year: 2000

Variable metric or quasi-Newton methods were originally developed for small- and moderate-size dense problems, their modifications based either on sparse, partitioned or limited-memory updates are very efficient on large-scale sparse problems. Very significant applications of these methods also appear in nonlinear least-squares approximation and nonsmooth optimization. An extensive review of variable metric methods and their use in various optimization fields.
Permanent Link: http://hdl.handle.net/11104/0123835