Methods for Linearly Constrained Problems

H. A. Eiselt and Carl-Louis Sandblom
Additional contact information
H. A. Eiselt: University of New Brunswick
Carl-Louis Sandblom: Dalhousie University

Chapter Chapter 6 in Nonlinear Optimization, 2019, pp 195-242 from Springer

Abstract: Abstract In this and the following two chapters, several algorithms for solving nonlinear constrained optimization problems will be described. First the most special of all constrained nonlinear programming problems is considered, namely the quadratic programming problem for which the objective function is convex and quadratic and the constraints are linear. In the second section of the chapter methods for the more general problem of optimizing a differentiable convex function subject to linear constraints are discussed. Although every convex quadratic programming problem could be solved also by these more general methods, it is generally preferable to employ quadratic programming methods when possible. As a general principle it is advisable to use more specialized techniques for more specialized problems. Consequently, for a given problem one should select a method (covered in the previous, this, or the next two chapters) from a box as high up in Table 6.1 as possible. The third section considers problems in which the objective function is quadratic, but concave, a difficult case.

Date: 2019
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:978-3-030-19462-8_6

Ordering information: This item can be ordered from
http://www.springer.com/9783030194628

DOI: 10.1007/978-3-030-19462-8_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 ().