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Zbl 1051.90017
Auslender, Alfred; Teboulle, Marc
Entropic proximal decomposition methods for convex programs and variational inequalities.
(English)
[J] Math. Program. 91, No. 1 (A), 33-47 (2001). ISSN 0025-5610; ISSN 1436-4646/e

Summary: We consider convex optimization and variational inequality problems with a given separable structure. We propose a new decomposition method for these problems which combines the recent logarithmic-quadratic proximal theory introduced by the authors with a decomposition method given by Chen-Teboulle for convex problems with particular structure. The resulting method allows to produce for the first time provably convergent decomposition schemes based on $C^\infty$ Lagrangians for solving convex structured problems. Under the only assumption that the primal-dual problems have nonempty solution sets, global convergence of the primal-dual sequences produced by the algorithm is established.
MSC 2000:
*90C25 Convex programming
49J40 Variational methods including variational inequalities

Keywords: convex optimization; decomposition methods; variational inequalities; entropic/interior proximal methods; Lagrangian multiplier methods

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