A conjugate-direction method based on a nonquadratic model
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A conjugate-gradient method for unconstrained optimization, which is based on a nonquadratic model, is proposed. The technique has the same properties as the Fletcher-Reeves algorithm when applied to a quadratic function. It is shown to be efficient when tried on general functions of different dimensionality.
Key WordsUnconstrained optimization conjugate-direction methods numerical algorithms rational models quadratic models
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- 1.Boland, W. R., Kamgnia, E. R., andKowalik, J. S.,A Conjugate-Gradient Optimization Method Invariant to Nonlinear Scaling, Journal of Optimization Theory and Applications, Vol. 27, No. 2, 1979.Google Scholar
- 3.Goldfarb, D.,Variable-Metric and Conjugate-Direction Methods in Unconstrained Optimization, Recent Developments, ACM Proceedings, National Meeting, Boston, Massachusetts, 1972.Google Scholar
- 5.Cheney, E. W.,Introduction to Approximation Theory, McGraw Hill, New York, New York, 1960.Google Scholar
- 6.Storey, C.,Optimization Using Rational Functions, Methods of Operations Research, Vol. 31, pp. 613–616, 1978.Google Scholar
- 8.White, B. F., andHolst, W. R., Paper Presented at the Joint Computer Conference, Washington, DC, 1964.Google Scholar
- 9.Colville, A. R.,A Comparative Study of Nonlinear Programming Codes, IBM New York Scientific Center, Technical Report No. 320-2949, 1968.Google Scholar
- 11.Cornwell, L. W.,An Acceleration Technique Applied to Conjugate-Direction Algorithms for Nonlinear Problems, Paper Presented at the 45th ORSA/TIMS Meeting, Boston, Massachusetts, 1974.Google Scholar