Note sur la convergence de méthodes de directions conjuguées

 Access to full text

 Full (PDF)

1. [1] R. FLETCHER et C. M. REEVES, « Function minimization by conjugate gradients», The Computer Journal, pp. 149-154 (1964). Zbl0132.11701MR187375
2. [2] E. POLAK, « On Primal and Dual Methods for solving discrete optimal control Problems», Second Int. Conf. on Computing methods in optimization problems, San Remo, Italy, Sept. 1968. Zbl0208.17403MR280243
3. [3] R. FLETCHER et M. J. D. POWELL, « rapidly convergent descent method for minimization», The Computer Journal, vol. 6, p. 163 (1963). Zbl0132.11603MR152116
4. [4] D. M. TOPKIS et A. F. VEINOTT (Jr), « On the convergence of some feasible direction algorithms for non-linear programming», Siam. J. on Control, vol. 5, n° 2, p. 268 (1967). Zbl0158.18805MR213161
5. [5] J. W. DANIEL, « The conjugate gradient method for linear and non linear operator equations», Siam J. Num. Anal, vol.4, n° 1(1967). Zbl0154.40302MR217987
6. [6] M. R. HESTENES et E. STIEFEL, « Methods of conjugate gradients for solving linear systems», J. Res. N. B. S., vol. 49, p. (1952). Zbl0048.09901MR60307
7. [7] T. GINSBURG, « The conjugate gradient method», Numerische Mathematik Band 5, Heft 2, p. 191 (1963). Zbl0123.11201MR154398

1. Jonas Koko, A conjugate gradient method with quasi-Newton approximation
2. Ladislav Lukšan, Variable metric method with limited storage for large-scale unconstrained minimization
3. Morteza Kimiaei, Majid Rostami, Impulse noise removal based on new hybrid conjugate gradient approach
4. Zainab Hassan Ahmed, Mohamed Hbaib, Khalil K. Abbo, A modified Fletcher-Reeves conjugate gradient method for unconstrained optimization with applications in image restoration
5. Youcef Elhamam Hemici, Samia Khelladi, Djamel Benterki, New hybrid conjugate gradient method for nonlinear optimization with application to image restoration problems
6. J. C. Ligeron, R. Goarin, Modélisation multidimensionnelle des taux de défaillance de composants électroniques
7. Fridrich Sloboda, An imperfect conjugate gradient algorithm
8. Ladislav Lukšan, Computational experience with improved conjugate gradient methods for unconstrained minimization