A fast dual proximal gradient algorithm for convex minimization and applications

Amir Beck*, Marc Teboulle

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

We consider the convex composite problem of minimizing the sum of a strongly convex function and a general extended valued convex function. We present a dual-based proximal gradient scheme for solving this problem. We show that although the rate of convergence of the dual objective function sequence converges to the optimal value with the rate O(1/k2), the rate of convergence of the primal sequence is of the order O(1/k).

Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalOperations Research Letters
Volume42
Issue number1
DOIs
StatePublished - 2014

Funding

FundersFunder number
Israel Science Foundation99812, 25312

    Keywords

    • Convex optimization
    • Dual-based methods
    • Fast gradient methods
    • Rate of convergence

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