Practical Aspects of an Interior-Point Method for Convex Programming

Abstract

We present an algorithm for solving convex programs with nonlinear constraints. The algorithm works in the primal space only and uses a predictor- corrector strategy to follow a smooth path that leads to an optimal solution. The algorithm simultaneously iterates towards feasibility and optimality. The matrices occurring in the algorithm can be kept sparse if the nonlinear functions are separable or depend on few variables only. Some promising numerical examples obtained from a preliminary implementation are included.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1991
Accession Number
ADA239457

Entities

People

  • Florian Jarre
  • Michael Saunders

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Arithmetic
  • Boundaries
  • Computations
  • Convergence
  • Convex Programming
  • Equations
  • Extrapolation
  • Guarantees
  • Iterations
  • Linear Programming
  • Linear Systems
  • Operations Research
  • Optimization
  • Perturbations
  • Sparse Matrix
  • United States

Readers

  • Operations Research

Technology Areas

  • Space
  • Space - Spacecraft Maneuvers