• Aucun résultat trouvé

Asymptotic convergence analysis of the proximal point algorithm

N/A
N/A
Protected

Academic year: 2021

Partager "Asymptotic convergence analysis of the proximal point algorithm"

Copied!
36
0
0

Texte intégral

Loading

Références

Documents relatifs

In the case where A is the normal cone of a nonempty closed convex set, this method reduces to a projec- tion method proposed by Sibony [23] for monotone variational inequalities

We give a proof of convergence that does not use the concept of partial inverse and show how to choose a scaling factor to accelerate the convergence in the strongly monotone

Besides, the method is tailor-made for finding only the average number of rounds required by the algorithm: the second moment (and a fortiori all other moments), and the

In doing so, we put to the fore inertial proximal algorithms that converge for general monotone inclusions, and which, in the case of convex minimization, give fast convergence rates

Clinical impact of thrombectomy in acute ST-elevation myocardial infarction: an individual patient-data pooled analysis of 11 trials. Randomized trial of a distal embolic

Pour ce qui est de l’analyse précise de l’influence de la cohésion du couple sur la socialisation de l’enfant envers ses pairs, qui était la quatrième

If the limit random variable takes values in a separable subset of ff, then strong convergence obtains for pramarts.. The martingale case of our results was

Among these methods, Platt’s Sequential Minimal Optimization (SMO) [8] is a simple algorithm where in each iteration only two variables are selected in the working set so