• Aucun résultat trouvé

Local Convergence Properties of Douglas–Rachford and Alternating Direction Method of Multipliers

N/A
N/A
Info
Télécharger
Protected

Academic year: 2021

Partager "Local Convergence Properties of Douglas–Rachford and Alternating Direction Method of Multipliers"

Copied!
34
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

Loading

Figure

Table 1 Examples of partly smooth functions. D DIF stands for the finite differences operator.
Fig. 2 Observed (solid) and predicted (dashed) convergence profiles of DR ( 3 ) in terms of ||z k − z ⋆ || with
Fig. 3 (a) Trajectories of {z k } k∈N and {x k } k∈N . The red segment is the set of minimizers and the blue
Fig. 5 Comparison between stationary (“S-DR”) and non-stationary DR (“NS-DR X”, X stands for Case X) when solving ( 22 ) with different functions J in Table 1

Références

Télécharger maintenant ( PDF - 34 Page - 700.52 KB )

Documents relatifs

Asynchronous alternating direction method of multipliers applied to the direct-current optimal power flow problem

The update steps of this algorithm inherits the principle of ADMM (i.e., alternating between the resolution of a primal and dual convex optimization problems), but at each

Convergence Rates of Forward-Douglas-Rachford Splitting Method

– Finite Time Activity Identification Under the assumption that the non- smooth component of the optimization problem is partly smooth around a global minimizer relative to its

Local Convergence Properties of Douglas–Rachford and Alternating Direction Method of Multipliers

The goal of this work is to understand the local linear convergence behaviour of Douglas–Rachford (resp. alternating direction method of multipliers) when the in- volved

Local Linear Convergence of Douglas-Rachford/ADMM for Low Complexity Regularization

Moreover, when these functions are partly polyhedral, we prove that DR (resp. ADMM) is locally linearly convergent with a rate in terms of the cosine of the Friedrichs angle between

Activity Identification and Local Linear Convergence of Douglas–Rachford/ADMM under Partial Smoothness

Keywords: Douglas–Rachford splitting, ADMM, Partial Smoothness, Finite Activity Identification, Local Linear Convergence..

ON THE LOCAL CONVERGENCE OF THE DOUGLAS–RACHFORD ALGORITHM

We will also show that for nonconvex sets A, B the Douglas–Rachford scheme may fail to converge and start to cycle without solving the feasibility problem.. We show that this may

Proximal point algorithm, Douglas–Rachford algorithm and alternating projections: a case study

In this paper, we consider the complementary problem of finding best case estimates, i.e., how slow the algorithm has to converge, and we also study exact asymptotic rates

ON LOCAL CONVERGENCE OF THE METHOD OF ALTERNATING PROJECTIONS

We propose a new geometric concept, called separable intersec- tion, which gives local convergence of alternating projections when combined with Hölder regularity, a mild