Globally Convergent Algorithms for Convex Programming.

Abstract

We consider solving a (minimization) convex program by sequentially solving a (minimization) convex approximating subproblem and then executing a line search. Each subproblem is constructed from the current estimate of a solution of the given problem, possibly together with other information. Under mild conditions, solving the current subproblem generates a descent direction for an exact penalty function. Minimizing the exact penalty function along the current descent direction provides a new estimate of a solution, and a new subproblem is formed. For any arbitrary starting estimate, this scheme generates a sequence of estimates that converges to a solution of the given problem. Moreover, it is not necesssary to require that the functions defining the given problem and each subproblem be differentiable. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1979
Accession Number
ADA068492

Entities

People

  • Eric Rosenberg

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Convergence
  • Convex Programming
  • Convex Sets
  • Directional
  • Geometric Programming
  • Hypotheses
  • Lagrangian Functions
  • Linear Programming
  • Nonlinear Programming
  • Numbers
  • Operations Research
  • Optimization
  • Real Numbers
  • Sequences
  • Systems Engineering

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Aviation Science / Aeronautics.
  • Calculus or Mathematical Analysis