A New Class of Feasible Direction Methods.

Abstract

This paper introduces a new class of feasible direction methods for solving differentiable convex programs with nonlinear convex constraints. The ones introduced here are based on two recent characterizations of optimality without a constraint qualification. The new methods are capable of generating feasible directions of descent along the boundary of the feasible set and they consistently give directions of steeper descent than many popular methods. This is achieved by solving only one linear program at each iteration. The new methods are particularly useful in solving large sparse convex programs; some of the programs tested had 100 variables and 50 nonlinear constraints. Morever, the new methods are applicable whether or not Slater's condition or any other constraint qualification is satisfied.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1977
Accession Number
ADA055032

Entities

People

  • A. Ben-tal
  • S. Zlobec

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Boundaries
  • Computer Programming
  • Computer Science
  • Computers
  • Convex Programming
  • Evolutionary Algorithms
  • Iterations
  • Linear Programming
  • Mathematics
  • New York
  • Nonlinear Programming
  • Optimization
  • Qualifications
  • Simplex Method
  • Two Dimensional

Readers

  • Operations Research