Convergence of Stochastic Proximal Gradient Algorithm

We study the extension of the proximal gradient algorithm where only a stochastic gradient estimate is available and a relaxation step is allowed. We establish convergence rates for function values in the convex case, as well as almost sure convergence and convergence rates for the iterates under fu...

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Veröffentlicht in:Applied mathematics & optimization Jg. 82; H. 3; S. 891 - 917
Hauptverfasser: Rosasco, Lorenzo, Villa, Silvia, Vũ, Bằng Công
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.12.2020
Springer Nature B.V
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ISSN:0095-4616, 1432-0606
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Abstract We study the extension of the proximal gradient algorithm where only a stochastic gradient estimate is available and a relaxation step is allowed. We establish convergence rates for function values in the convex case, as well as almost sure convergence and convergence rates for the iterates under further convexity assumptions. Our analysis avoid averaging the iterates and error summability assumptions which might not be satisfied in applications, e.g. in machine learning. Our proofing technique extends classical ideas from the analysis of deterministic proximal gradient algorithms.
AbstractList We study the extension of the proximal gradient algorithm where only a stochastic gradient estimate is available and a relaxation step is allowed. We establish convergence rates for function values in the convex case, as well as almost sure convergence and convergence rates for the iterates under further convexity assumptions. Our analysis avoid averaging the iterates and error summability assumptions which might not be satisfied in applications, e.g. in machine learning. Our proofing technique extends classical ideas from the analysis of deterministic proximal gradient algorithms.
Author Villa, Silvia
Vũ, Bằng Công
Rosasco, Lorenzo
Author_xml – sequence: 1
  givenname: Lorenzo
  surname: Rosasco
  fullname: Rosasco, Lorenzo
  organization: DIBRIS, Università di Genova, LCSL, Istituto Italiano di Tecnologia and Massachusetts Institute of Technology
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  givenname: Silvia
  surname: Villa
  fullname: Villa, Silvia
  email: silvia.villa@unige.it
  organization: Dipartimento di Matematica, Università di Genova
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  givenname: Bằng Công
  surname:
  fullname: Vũ, Bằng Công
  organization: EPFL STI IEL LIONS, ELD 243 (Batiment EL)
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Issue 3
Keywords Forward–backward splitting algorithm
Stochastic optimization
Proximal methods
Language English
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Snippet We study the extension of the proximal gradient algorithm where only a stochastic gradient estimate is available and a relaxation step is allowed. We establish...
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SubjectTerms Algorithms
Calculus of Variations and Optimal Control; Optimization
Control
Convergence
Convexity
Machine learning
Mathematical and Computational Physics
Mathematical Methods in Physics
Mathematics
Mathematics and Statistics
Numerical and Computational Physics
Simulation
Systems Theory
Theoretical
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Title Convergence of Stochastic Proximal Gradient Algorithm
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