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	NDA: Algorithms for Nondifferentiable Optimization Lukšan, Ladislav ; Vlček, Jan Fulltext: content.csg - PDF Plný tet: v797-00 - PDF Detailed record
	Variable Metric Method for Nonconvex Nonsmooth Unconstrained Minimization Vlček, Jan ; Lukšan, Ladislav A special variable metric method is given for finding 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. Some variable numerical experience is reported. Detailed record
	Variable Metric Methods for Unconstrained Optimization Lukšan, Ladislav ; Spedicato, E. ; Vlček, Jan Fulltext: content.csg - PDF Plný tet: v780-99 - PDF Detailed record
	Application of variable metric methods for nonsmooth minimization Lukšan, Ladislav ; Vlček, Jan Detailed record
	Globally Convergent Variable Metric Method for Convex Nonsmooth Unconstrained Minimization Lukšan, Ladislav ; Vlček, Jan Fulltext: content.csg - PDF Plný tet: v741-98 - PDF Detailed record
	PMIN - A Recursive Quadratic Programming Variable Metric Algorithm for Minimax Optimization Lukšan, Ladislav ; Vlček, Jan Fulltext: content.csg - PDF Plný tet: v717-97 - PDF Detailed record
	Simple Scaling for Variable Metric Updates Lukšan, Ladislav ; Vlček, Jan Fulltext: content.csg - PDF Plný tet: v611-95 - PDF Detailed record
	Efficient Methods for Large-Scale Unconstrained Optimization Lukšan, Ladislav ; Vlček, Jan Fulltext: content.csg - PDF Plný tet: v915-04 - PDF Detailed record
	Shifted Variable Metric Methods for Unconstrained Optimization Lukšan, Ladislav ; Vlček, Jan A family of shifted variable metric methods for unconstrained optimization is investigated. These methods form a basis for shifted limited-memory variable metric methods introduced in second contribution in proceedings of SANM 2003. We describe basic properties of these methods, establish their global convergence and give conditions for the superlinear rate of convergence. Their efficiency is demonstrated by using extensive numerical experiments. Detailed record
	New Limited-Memory Variable Metric Methods for Unconstrained Optimization Vlček, Jan ; Lukšan, Ladislav A new family of numerically efficient variable metric or quasi-Newton methods for unconstrained optimizaion is adapted for large-scale optimization. Global convergence of the method can be established for convex sufficiently smooth functions. Some encouraging numerical experience is reported. We refer to our report V-876 for proof and details. Detailed record

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