Bi-affine scaling iterative method for convex quadratic programming with bound constraints

Hongwei Yue and Peiping Shen

Mathematics and Computers in Simulation (MATCOM), 2024, vol. 226, issue C, 373-382

Abstract: To solve general convex quadratic programming problems with bound constraints, this paper proposes a new interior point iterative method that is easy to be implemented. The method exhibits a simple and sufficiently smooth search direction, and possesses the characteristics of affine scaling. Under the limited optimal stepsize rule, starting from an arbitrary interior point, any accumulation point of the generated sequence is an optimal solution of the corresponding problem. Furthermore, due to the absence of introducing dual variables and solving equations, the proposed method is more suitable for solving large-scale problems. Preliminary numerical results indicate that the new method has advantages in terms of both efficiency and accuracy.

Keywords: Convex quadratic programming; Bound constraints; Interior-point algorithm; Affine scaling (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475424002647
Full text for ScienceDirect subscribers only

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:eee:matcom:v:226:y:2024:i:c:p:373-382

DOI: 10.1016/j.matcom.2024.07.013

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().