Global Convergence for Newton Methods in Mathematical Programming,

Abstract

In constrained optimization problems in mathematical programming one wants to minimize a functional f(x) over a given set C. If, at an approximate solution (x sub n), one replaces f(x) by its Taylor series expansion through quadratic terms at (x sub n) and denotes by x sub (n+1) the minimizing point for this over C, one has a direct analogue of Newton's method. The local convergence of this has been previously analyzed; here the author gives global convergence results for this and the similar algorithm in which the constraint set C is also linearized at each step. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1972
Accession Number
AD0746479

Entities

People

  • James W. Daniel

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Analogs
  • Computer Programming
  • Convergence
  • Evolutionary Algorithms
  • Heuristic Methods
  • Mathematical Programming
  • Mathematics
  • Optimization

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Calculus or Mathematical Analysis
  • Linear Algebra