Among practical methods for large-scale Nondifferentiable Optimization (NDO), Bundle methods are widely recognized to play a relevant role; despite thier potential, however, they are not often utilized for maximization of polyhedral functions, that appears in many different contexts such as Lagrangian Duals and decomposition algorithms, since simpler-to-program but less efficient approaches like subgradient methods are preferred. The aim of this work is to provide an applications-oriented survey of the theory of Bundle methods when applied to problems arising in continuous and combinatorial optimization, with an introduction to the several variants of Bundle approaches that can be built up by using a limited set of basic concepts and tools
Applying Bundle Methods to Optimization of Polyhedral Functions: An Applications-Oriented Development
FRANGIONI, ANTONIO;
1995-01-01
Abstract
Among practical methods for large-scale Nondifferentiable Optimization (NDO), Bundle methods are widely recognized to play a relevant role; despite thier potential, however, they are not often utilized for maximization of polyhedral functions, that appears in many different contexts such as Lagrangian Duals and decomposition algorithms, since simpler-to-program but less efficient approaches like subgradient methods are preferred. The aim of this work is to provide an applications-oriented survey of the theory of Bundle methods when applied to problems arising in continuous and combinatorial optimization, with an introduction to the several variants of Bundle approaches that can be built up by using a limited set of basic concepts and toolsI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
