Convex

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Source URL: convex.ipa.or.id

- Date: 2018-04-27 07:37:36
    2Statistical Performance of Convex Tensor Decomposition Ryota Tomioka† Taiji Suzuki† Department of Mathematical Informatics, The University of Tokyo

    Statistical Performance of Convex Tensor Decomposition Ryota Tomioka† Taiji Suzuki† Department of Mathematical Informatics, The University of Tokyo

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    Source URL: tomioka.dk

    Language: English - Date: 2017-08-10 12:21:48
      3ON THE LASSERRE HIERARCHY OF SEMIDEFINITE PROGRAMMING RELAXATIONS OF CONVEX POLYNOMIAL OPTIMIZATION PROBLEMS ETIENNE DE KLERK∗ AND MONIQUE LAURENT† Abstract. The Lasserre hierarchy of semidefinite programming approxi

      ON THE LASSERRE HIERARCHY OF SEMIDEFINITE PROGRAMMING RELAXATIONS OF CONVEX POLYNOMIAL OPTIMIZATION PROBLEMS ETIENNE DE KLERK∗ AND MONIQUE LAURENT† Abstract. The Lasserre hierarchy of semidefinite programming approxi

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      Source URL: homepages.cwi.nl

      Language: English - Date: 2011-06-06 01:41:37
        4A Lower Bound for the Optimization of Finite Sums A. Optimization of a strongly convex smooth functions The most accessible derivation of this classic lower bound (Nesterov, 2004) relies on the simplifying assumption th

        A Lower Bound for the Optimization of Finite Sums A. Optimization of a strongly convex smooth functions The most accessible derivation of this classic lower bound (Nesterov, 2004) relies on the simplifying assumption th

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        Source URL: proceedings.mlr.press

        Language: English - Date: 2017-05-06 17:27:03
          5Convex Tensor Decomposition via Structured Schatten Norm Regularization Ryota Tomioka Toyota Technological Institute at Chicago Chicago, IL 60637

          Convex Tensor Decomposition via Structured Schatten Norm Regularization Ryota Tomioka Toyota Technological Institute at Chicago Chicago, IL 60637

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          Source URL: tomioka.dk

          Language: English - Date: 2017-08-10 12:21:48
            6Hausdorff Center for Mathematics, Summer School (May 9–13, 2016) Problems for “Discrete Convex Analysis” (by Kazuo Murota) Problem 1. Prove that a function f : Z2 → R defined by f (x1 , x2 ) = φ(x1 − x2 ) is

            Hausdorff Center for Mathematics, Summer School (May 9–13, 2016) Problems for “Discrete Convex Analysis” (by Kazuo Murota) Problem 1. Prove that a function f : Z2 → R defined by f (x1 , x2 ) = φ(x1 − x2 ) is

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            Source URL: www.comp.tmu.ac.jp

            Language: English - Date: 2016-05-09 03:17:31
              7Linearly Constrained Convex Programming with Python October 14, 2017 1

              Linearly Constrained Convex Programming with Python October 14, 2017 1

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              Source URL: www.shanshu.ai

              Language: English - Date: 2018-07-19 05:09:01
                8610 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 58, NO. 3, MARCH 2013 Reaching an Optimal Consensus: Dynamical Systems That Compute Intersections of Convex Sets

                610 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 58, NO. 3, MARCH 2013 Reaching an Optimal Consensus: Dynamical Systems That Compute Intersections of Convex Sets

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                Source URL: users.cecs.anu.edu.au

                Language: English - Date: 2013-02-25 09:52:16
                  9How elegant modern convex analysis was influenced by Moreau’s seminal work. Samir ADLY University of Limoges, France

                  How elegant modern convex analysis was influenced by Moreau’s seminal work. Samir ADLY University of Limoges, France

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                  Source URL: www.unilim.fr

                  - Date: 2014-07-21 12:57:21
                    10Information Complexity of Black-Box Convex Optimization: A New Look Via Feedback Information Theory

                    Information Complexity of Black-Box Convex Optimization: A New Look Via Feedback Information Theory

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                    Source URL: maxim.ece.illinois.edu

                    Language: English - Date: 2012-04-18 12:07:58