The Computer Can Differentiate Too

Abstract

For mathematical problems whose solution requires the evaluation of first or second partial derivatives of user-defined functions, symbolic differentiation is offered as a viable alternative to numerical differencing techniques and user-written subprograms. The paper describes an efficient user- oriented approach, equivalent to symbolic differentiation, for the generation of code capable of evaluating partial derivatives of user-specified functions. Computational results of an experimental implementation of the method are reported. The feasibility of this approach is additionally demonstrated by interfacing the experimental code with the Sequential Unconstrained Minimization Technique of Fiacco and McCormick for the solution of nonlinear programming problems and results are reported.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1974
Accession Number
AD0783076

Entities

People

  • Darwin Dee Klingman
  • Lee Litzler

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Assembly Languages
  • Calculus
  • Computer Programming
  • Computer Programs
  • Computers
  • Differential Equations
  • Digital Computers
  • Language
  • Machine Languages
  • Military Research
  • New York
  • Nonlinear Programming
  • Notation
  • Optimization
  • Programming Languages
  • Test And Evaluation

Readers

  • Computational Linguistics
  • Computational Modeling and Simulation
  • Operations Research