On the Identification of Local Minimizers in Inertia-Controlling Methods for Quadratic Programming

Abstract

The verification of a local minimizer of a general (i.e., nonconvex) quadratic program is in general an NP-hard problem. The difficulty concerns the optimality of certain points (which we call dead points) at which the first-order necessary conditions for optimality are satisfied, but strict complementarity does not hold. One important class of methods for solving general quadratic programming problems are called intertia-controlling quadratic programming (ICQP) methods. We derive a computational scheme for proceeding at a dead point that is appropriate for general ICQP method.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1989
Accession Number
ADA212514

Entities

People

  • Anders L. Forsgren
  • Philip Edward Gill
  • Walter Murray

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computations
  • Computer Programming
  • Computer Science
  • Eigenvalues
  • Equations
  • Identification
  • Linear Algebra
  • Linear Programming
  • Mathematical Programming
  • Mathematics
  • Operations Research
  • Optimization
  • Quadratic Programming
  • Simplex Method
  • Verification

Fields of Study

  • Mathematics

Readers

  • Operations Research