Convex

Results: 2611



#Item
21CVXcanon: Automatic Canonicalization of Disciplined Convex Programs John Miller Paul Quigley

CVXcanon: Automatic Canonicalization of Disciplined Convex Programs John Miller Paul Quigley

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Source URL: stanford.edu

Language: English - Date: 2018-04-08 01:24:19
    22A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming arXiv:1804.08544v2 [math.OC] 16 MayAlp Yurtsever∗

    A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming arXiv:1804.08544v2 [math.OC] 16 MayAlp Yurtsever∗

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    Source URL: arxiv.org

    Language: English - Date: 2018-05-17 00:13:37
      23⋆ Combinatorial Optimization (Oberwolfach, November 9–15, Discrete DC Programming by Discrete Convex Analysis —Use of Conjugacy— Kazuo Murota (U. Tokyo)

      ⋆ Combinatorial Optimization (Oberwolfach, November 9–15, Discrete DC Programming by Discrete Convex Analysis —Use of Conjugacy— Kazuo Murota (U. Tokyo)

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

      Language: English - Date: 2014-11-13 00:32:38
        24Line Search for Averaged Operator Iteration Pontus Giselsson, Mattias F¨alt, and Stephen Boyd Abstract Many popular first order algorithms for convex optimization, such as forward-backward splitting, Douglas-Rachford sp

        Line Search for Averaged Operator Iteration Pontus Giselsson, Mattias F¨alt, and Stephen Boyd Abstract Many popular first order algorithms for convex optimization, such as forward-backward splitting, Douglas-Rachford sp

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        Source URL: www.control.lth.se

        Language: English - Date: 2016-06-27 06:20:10
          251 date: April 2, 2015 L-CONVEX FUNCTIONS AND MCONVEX FUNCTIONS In the field of nonlinear programming (in continuous variables) convex analysis

          1 date: April 2, 2015 L-CONVEX FUNCTIONS AND MCONVEX FUNCTIONS In the field of nonlinear programming (in continuous variables) convex analysis

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

          Language: English - Date: 2015-04-02 00:33:58
            26A polynomial-time inexact primal-dual infeasible path-following algorithm for convex quadratic SDP Lu Li ∗, and Kim-Chuan Toh† 1 OctoberAbstract

            A polynomial-time inexact primal-dual infeasible path-following algorithm for convex quadratic SDP Lu Li ∗, and Kim-Chuan Toh† 1 OctoberAbstract

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            Source URL: www.math.nus.edu.sg

            Language: English - Date: 2009-10-01 06:49:01
              27Continuous Games Convex Games Aggregative Nash Games Distributed synchronous algorithm Distributed asynchronous algorithm

              Continuous Games Convex Games Aggregative Nash Games Distributed synchronous algorithm Distributed asynchronous algorithm

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              Source URL: www.ifp.illinois.edu

              Language: English - Date: 2015-07-28 08:01:52
                28MITSUBISHI ELECTRIC RESEARCH LABORATORIES ADMM for General Convex QPs - Optimal Convergence, Infeasibility Detection and Acceleration Arvind U. Raghunathan

                MITSUBISHI ELECTRIC RESEARCH LABORATORIES ADMM for General Convex QPs - Optimal Convergence, Infeasibility Detection and Acceleration Arvind U. Raghunathan

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                Source URL: www.imtlucca.it

                Language: English - Date: 2016-06-24 04:39:56
                  29Convex Factorization Machines Mathieu Blondel, Akinori Fujino, and Naonori Ueda NTT Communication Science Laboratories, Kyoto, Japan Abstract. Factorization machines are a generic framework which allows to mimic many fac

                  Convex Factorization Machines Mathieu Blondel, Akinori Fujino, and Naonori Ueda NTT Communication Science Laboratories, Kyoto, Japan Abstract. Factorization machines are a generic framework which allows to mimic many fac

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                  Source URL: mblondel.org

                  Language: English - Date: 2018-08-14 03:15:41
                    30A Semismooth Newton Method for Fast, Generic Convex Programming Alnur Ali * 1 Eric Wong * 1 J. Zico Kolter 2 i.e., the space of m × m positive semidefinite matrices Sm +, yields a semidefinite program; and taking K as

                    A Semismooth Newton Method for Fast, Generic Convex Programming Alnur Ali * 1 Eric Wong * 1 J. Zico Kolter 2 i.e., the space of m × m positive semidefinite matrices Sm +, yields a semidefinite program; and taking K as

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                    Source URL: proceedings.mlr.press

                    Language: English - Date: 2018-02-06 15:06:57