A Feasible Direction Subgradient Algorithm for a Class of Nondifferentiable Optimization Problems.

Abstract

An implementable feasible direction subgradient algorithm is presented for minimizing the maximum of a finite collection of functions subject to constraints. It is assumed that each function involved in defining the objective function is the sum of a finite collection of basic convex functions and that the number of different subgradient sets associated with nondifferentiable points of each basic function is finite on any bounded set. It is demonstrated that under certain conditions, including continuous differentiability of the constraints and a regularity condition of the feasible region, that the algorithm generates a feasible sequence which converges to an E-optimal solution. The results of some computational experiments are included.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1978
Accession Number
ADA062089

Entities

People

  • Donald Hearn
  • Jacques Chatelon
  • Timothy J. Lowe

Organizations

  • University of Florida

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  • Air Platforms

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Convergence
  • Engineering
  • Heuristic Methods
  • Industrial Engineering
  • Iterations
  • Military Research
  • Nonlinear Programming
  • Notation
  • Optimization
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  • Stationary
  • Systems Engineering

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  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Calculus or Mathematical Analysis
  • International Relations and European Studies