Model Building and Practical Aspects of Nonlinear Programming.

Abstract

Many optimization problems arise from complex models of real-world phenomena. This paper examines the close relationship between certain features of well-posed models and robust optimization methods. First, a list of modelling principles is given, to aid in formulating models suited to solution by modern optimization methods. Quasi-Newton sequential quadratic programming methods for nonlinearly constrained optimization are then discussed. The topics considered include representation and definition of the approximate Hessian of the Lagrangian function; similarities between primal and dual quadratic programming methods; treatment of inconsistent and ill-conditioned subproblems; and properties of various active-set strategies. Finally, the results of solving several test problems are analyzed in detail, including significant characteristics of the overall solution process such as superlinear convergence. Keywords: Sequential quadratic programming; Quasi-Newton methods; Numerical methods; Optimization. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1985
Accession Number
ADA155720

Entities

People

  • M. H. Wright
  • Mark A. Saunders
  • P. E. Gill
  • William J. Murray

Organizations

  • Stanford University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Computations
  • Computer Programming
  • Contracts
  • Convergence
  • Estimators
  • Lagrangian Functions
  • Linear Programming
  • Mathematical Models
  • Mathematical Programming
  • Military Research
  • New York
  • Nonlinear Programming
  • Operations Research
  • Optimization
  • Quadratic Programming

Fields of Study

  • Mathematics

Readers

  • Operations Research
  • Systems Analysis and Design