Pseudo-Boolean Optimization in Case of an Unconnected Feasible Set

Alexander Antamoshkin () and Igor Masich
Additional contact information
Alexander Antamoshkin: Siberian State Aerospace University
Igor Masich: Siberian State Aerospace University

A chapter in Models and Algorithms for Global Optimization, 2007, pp 111-122 from Springer

Abstract: Abstract Unconstrained pseudo-Boolean optimization is an issue that studied enough now. Algorithms that have been designed and investigated in the area of unconstrained pseudo-Boolean optimization are applied successfully for solving various problems. Particularly, these are local optimization methods [AL97, AM04a, PS82] and stochastic and regular algorithms based on local search for special function classes [ASSSO, BSV02, WW02]. Moreover, there is a number of algorithms for optimization of functions given in explicit form: Hammer’s basic algorithm that, was introduced in [HR68] and simplified in [BHO2]; algorithms for optimization of quadratic functions [AFLS0l, FH00, HS89], etc. Universal optimization methods are also used successfully: genetic algorithms, simulated annealing, tabu search [Gol89, Sch95].

Keywords: Local Search; Greedy Algorithm; Constraint Function; Accessible Region; Unimodal Function (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-0-387-36721-7_7

Ordering information: This item can be ordered from
http://www.springer.com/9780387367217

DOI: 10.1007/978-0-387-36721-7_7

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().