Globally convergent variable metric method for nonconvex nondifferentiable unconstrained minimization

Globally convergent variable metric method for nonconvex nondifferentiable unconstrained minimization

A special variable metric method is given for finding the stationary points of locally Lipschitz continuous functions which are not necessarily convex or differentiable. Time consuming quadratic programming subproblems do not need to be solved. Global convergence of the method is established. Some e...

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Veröffentlicht in:Journal of optimization theory and applications 2001-11, Vol.111 (2), p.407-430
Hauptverfasser: VLCEK, J, LUKSAN, L
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Applied sciences
Efficiency
Exact sciences and technology
Experiments
Mathematical programming
Mathematics
Methods
Miscellaneous
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Operational research and scientific management
Operational research. Management science
Quadratic programming
Sciences and techniques of general use
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Beschreibung
Zusammenfassung:A special variable metric method is given for finding the stationary points of locally Lipschitz continuous functions which are not necessarily convex or differentiable. Time consuming quadratic programming subproblems do not need to be solved. Global convergence of the method is established. Some encouraging numerical experience is reported.
ISSN:0022-3239
1573-2878
DOI:10.1023/a:1011990503369