Strong Convergence of Projected Subgradient Methods for Nonsmooth and Nonstrictly Convex Minimization View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2008-12

AUTHORS

Paul-Emile Maingé

ABSTRACT

In this paper, we establish a strong convergence theorem regarding a regularized variant of the projected subgradient method for nonsmooth, nonstrictly convex minimization in real Hilbert spaces. Only one projection step is needed per iteration and the involved stepsizes are controlled so that the algorithm is of practical interest. To this aim, we develop new techniques of analysis which can be adapted to many other non-Fejérian methods. More... »

PAGES

899-912

References to SciGraph publications

  • 2001-12. Extension of Subgradient Techniques for Nonsmooth Optimization in Banach Spaces in SET-VALUED ANALYSIS
  • 2009-02-25. Projection methods for variational inequalities with application to the traffic assignment problem in NONDIFFERENTIAL AND VARIATIONAL TECHNIQUES IN OPTIMIZATION
  • 2004-09. The Gradient Projection Method with Exact Line Search in JOURNAL OF GLOBAL OPTIMIZATION
  • 1993-02. Convergence of some algorithms for convex minimization in MATHEMATICAL PROGRAMMING
  • 1966-07. Methods of solution of nonlinear extremal problems in CYBERNETICS AND SYSTEMS ANALYSIS
  • 1998-09. Error Stability Properties of Generalized Gradient-Type Algorithms in JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
  • 1998-03. On the projected subgradient method for nonsmooth convex optimization in a Hilbert space in MATHEMATICAL PROGRAMMING
  • Journal

    TITLE

    Set-Valued Analysis

    ISSUE

    7-8

    VOLUME

    16

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11228-008-0102-z

    DOI

    http://dx.doi.org/10.1007/s11228-008-0102-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1020109765


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