Conic Linear Programming

David G. Luenberger and Yinyu Ye
Additional contact information
David G. Luenberger: Stanford University
Yinyu Ye: Stanford University

Chapter Chapter 6 in Linear and Nonlinear Programming, 2021, pp 165-198 from Springer

Abstract: Abstract Conic Linear Programming, hereafter CLP, is a natural extension of Linear programming (LP). In LP, the variables form a vector which is required to be component-wise nonnegative, while in CLP they are points in a pointed convex cone (see Appendix B.1 ) of an Euclidean space, such as vectors as well as matrices of finite dimensions. For example, Semidefinite programming (SDP) is a kind of CLP, where the variable points are symmetric matrices constrained to be positive semidefinite.

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:isochp:978-3-030-85450-8_6

Ordering information: This item can be ordered from
http://www.springer.com/9783030854508

DOI: 10.1007/978-3-030-85450-8_6

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().