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Greedy Strategies for Convex Optimization

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Abstract

We investigate two greedy strategies for finding an approximation to the minimum of a convex function E defined on a Hilbert space H. We prove convergence rates for these algorithms under suitable conditions on the objective function E. These conditions involve the behavior of the modulus of smoothness and the modulus of uniform convexity of E.

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Notes

  1. Here and later we will use the abbreviation (co) if an algorithm is used for convex optimization.

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Author information

Authors and Affiliations

  1. WorldQuant LLC, Hanoi, Vietnam

    Hao Nguyen

  2. Department of Mathematics, Texas A&M University, College Station, TX, 77843, USA

    Guergana Petrova

Authors
  1. Hao Nguyen
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  2. Guergana Petrova
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Corresponding author

Correspondence to Guergana Petrova.

Additional information

This research was supported by the Office of Naval Research Contract ONR N00014-11-1-0712, by the NSF Grant DMS 1521067, and by the Bulgarian Science Fund Grant DFNI-T01/0001.

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Nguyen, H., Petrova, G. Greedy Strategies for Convex Optimization. Calcolo 54, 207–224 (2017). https://doi.org/10.1007/s10092-016-0183-2

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  • DOI: https://doi.org/10.1007/s10092-016-0183-2

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