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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
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- 한국산업응용수학회 JOURNAL OF THE KOREAN SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS Journal of the Korean Society for Industrial and Applied Mathematics Vol.8 No.2
- 발행연도
- 2004.12
- 수록면
- 7 - 13 (7page)
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초록· 키워드
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A Conjugate Gradient (CG) algorithm for unconstrained minimization is proposed which is invariant to a nonlinear scaling of a strictly convex quadratic function and which generates mutually conjugate directions for extended quadratic functions. It is derived for inexact line searches and is designed for the minimization of general nonlinear functions. It compares favorably in numerical tests with the original Dixon algorithm on which the new algorithm is based.
목차
ABSTRACT
1. INTRODUCTION
2. Derivation of σi
3. ALGORITHM EDX
4. COMPUTATIONAL RESULTS AND CONCLUSION
REFERENCES
Appendix
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