Submodular functions and convexity

L. Lovász
Additional contact information
L. Lovász: Eötvös Loránd University, Department of Analysis I

A chapter in Mathematical Programming The State of the Art, 1983, pp 235-257 from Springer

Abstract: Abstract In “continuous” optimization convex functions play a central role. Besides elementary tools like differentiation, various methods for finding the minimum of a convex function constitute the main body of nonlinear optimization. But even linear programming may be viewed as the optimization of very special (linear) objective functions over very special convex domains (polyhedra). There are several reasons for this popularity of convex functions: Convex functions occur in many mathematical models in economy, engineering, and other sciencies. Convexity is a very natural property of various functions and domains occuring in such models; quite often the only non-trivial property which can be stated in general.

Keywords: Polynomial Time; Convex Function; Rank Function; Modular Function; Submodular Function (search for similar items in EconPapers)
Date: 1983
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:sprchp:978-3-642-68874-4_10

Ordering information: This item can be ordered from
http://www.springer.com/9783642688744

DOI: 10.1007/978-3-642-68874-4_10

Access Statistics for this chapter

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