Convex Separable Programming

Stefan M. Stefanov ()
Additional contact information
Stefan M. Stefanov: South-West University Neofit Rilski

Chapter Chapter 3 in Separable Optimization, 2021, pp 73-84 from Springer

Abstract: Abstract AsSeparable programmingconvex it was pointed out in Chapter Two, if $$f_j (x_j)$$ f j ( x j ) are strictly convex and $$g_{ij} (x_j)$$ g ij ( x j ) are convex for $$i = 1, \ldots , m$$ i = 1 , … , m and $$j \not \in \mathcal{L}$$ j ∉ L in problem (SP) ( 2.5 )–( 2.7 ), the standard simplex method, discarding the restricted basis entry rule, is applicable to the approximating linear program (LASP) ( 2.17 )–( 2.21 ).

Date: 2021
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:978-3-030-78401-0_3

Ordering information: This item can be ordered from
http://www.springer.com/9783030784010

DOI: 10.1007/978-3-030-78401-0_3

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 ().