We analyze the convergence of the prox-regularization algorithms introduced in [1], to solve generalized fractional programs, without assuming that the optimal solutions set of the considered problem is nonempty, and since the objective functions are variable with respect to the iterations in the auxiliary problems generated by Dinkelbach-type algorithms DT1 and DT2, we consider that the regularizing parameter is also variable. On the other hand we study the convergence when the iterates are only -minimizers of the auxiliary problems. This situation is more general than the one considered in [1]. We also give some results concerning the rate of convergence of these algorithms, and show that it is linear and some times superlinear for some classes of functions. Illustrations by numerical examples are given in [1].
@article{RO_2002__36_1_73_0, author = {Roubi, Ahmed}, title = {Convergence of prox-regularization methods for generalized fractional programming}, journal = {RAIRO - Operations Research - Recherche Op\'erationnelle}, pages = {73--94}, publisher = {EDP-Sciences}, volume = {36}, number = {1}, year = {2002}, doi = {10.1051/ro:2002006}, mrnumber = {1920380}, zbl = {1103.90401}, language = {en}, url = {http://archive.numdam.org/articles/10.1051/ro:2002006/} }
TY - JOUR AU - Roubi, Ahmed TI - Convergence of prox-regularization methods for generalized fractional programming JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2002 SP - 73 EP - 94 VL - 36 IS - 1 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/ro:2002006/ DO - 10.1051/ro:2002006 LA - en ID - RO_2002__36_1_73_0 ER -
%0 Journal Article %A Roubi, Ahmed %T Convergence of prox-regularization methods for generalized fractional programming %J RAIRO - Operations Research - Recherche Opérationnelle %D 2002 %P 73-94 %V 36 %N 1 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/ro:2002006/ %R 10.1051/ro:2002006 %G en %F RO_2002__36_1_73_0
Roubi, Ahmed. Convergence of prox-regularization methods for generalized fractional programming. RAIRO - Operations Research - Recherche Opérationnelle, Tome 36 (2002) no. 1, pp. 73-94. doi : 10.1051/ro:2002006. http://archive.numdam.org/articles/10.1051/ro:2002006/
[1] Prox-Regularization Methods for Generalized Fractional Programming. J. Optim. Theory Appl. 99 (1998) 691-722. | MR | Zbl
,[2] An Algorithm for Generalized Fractional Programs. J. Optim. Theory Appl. 47 (1985) 35-49. | MR | Zbl
, and ,[3] Note on an Algorithm for Generalized Fractional Programs. J. Optim. Theory Appl. 50 (1986) 183-187. | MR | Zbl
, and ,[4] On Nonlinear Fractional Programming. Management Sci. 13 (1967) 492-498. | MR | Zbl
,[5] Method of Centers for Generalized Fractional Programming. J. Optim. Theory Appl. 107 (2000) 123-143. | MR | Zbl
,[6] Régularisation d'Inéquations Variationnelles par Approximation Successives. RAIRO: Oper. Res. 4 (1970) 154-158. | Numdam | Zbl
,[7] Monotone Operators and the Proximal Point Algorithm. SIAM J. Control Optim. 14 (1976) 877-898. | MR | Zbl
,[8] On the Convergence of the Proximal Point Algorithm for Convex Minimization. SIAM J. Control Optim. 29 (1991) 403-419. | MR | Zbl
,[9] Stable Methods for Ill-Posed Variational Problems. Akademic Verlag, Berlin, Germany (1994). | MR | Zbl
and ,[10] Practical Aspects of the Moreau-Yosida Regularization: Theoretical Preliminaries. SIAM J. Optim. 7 (1997) 367-385. | Zbl
and ,[11] Analyse Convexe et Problèmes Variationnels. Gauthier-Villars, Paris, Bruxelles, Montréal (1974). | MR | Zbl
and ,[12] Weak Sharp Minima in Mathematical Programming. SIAM J. Control Optim. 31 (1993) 1340-1359. | MR | Zbl
and ,[13] Conditioning and Upper-Lipschitz Inverse Subdifferentials in Nonsmooth Optimization Problems. J. Optim. Theory Appl. 95 (1997) 127-148. | MR | Zbl
, and ,Cité par Sources :