Restricted simplicial decomposition for convex constrained problems

Jose Antonio Ventura, Donald W. Hearn

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

The strategy of Restricted Simplicial Decomposition is extended to convex programs with convex constraints. The resulting algorithm can also be viewed as an extension of the (scaled) Topkis-Veinott method of feasible directions in which the master problem involves optimization over a simplex rather than the usual line search. Global convergence of the method is proven and conditions are given under which the master problem will be solved a finite number of times. Computational testing with dense quadratic problems confirms that the method dramatically improves the Topkis-Veinott algorithm and that it is competitive with the generalized reduced gradient method.

Original languageEnglish (US)
Pages (from-to)71-85
Number of pages15
JournalMathematical Programming
Volume59
Issue number1-3
DOIs
StatePublished - Mar 1 1993

Fingerprint

Method of Feasible Directions
Decomposition
Decompose
Convex Constraints
Convex Program
Gradient methods
Line Search
Gradient Method
Global Convergence
Optimization Problem
Testing
Strategy

All Science Journal Classification (ASJC) codes

  • Software
  • Mathematics(all)

Cite this

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Restricted simplicial decomposition for convex constrained problems. / Ventura, Jose Antonio; Hearn, Donald W.

In: Mathematical Programming, Vol. 59, No. 1-3, 01.03.1993, p. 71-85.

Research output: Contribution to journalArticle

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