Convex optimization

Results: 1096



#Item
991A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games H. Brendan McMahan∗ Google, Inc[removed]Forbes Avenue

A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games H. Brendan McMahan∗ Google, Inc[removed]Forbes Avenue

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Source URL: www.cs.cmu.edu

Language: English - Date: 2007-06-26 10:47:41
992THE MAXIMAL DENSITY OF PRODUCT-FREE SETS IN Z/nZ PÄR KURLBERG, JEFFREY C. LAGARIAS, AND CARL POMERANCE A BSTRACT. This paper studies the maximal size of product-free sets in Z/nZ. These are sets of residues for which th

THE MAXIMAL DENSITY OF PRODUCT-FREE SETS IN Z/nZ PÄR KURLBERG, JEFFREY C. LAGARIAS, AND CARL POMERANCE A BSTRACT. This paper studies the maximal size of product-free sets in Z/nZ. These are sets of residues for which th

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Source URL: www.math.dartmouth.edu

Language: English - Date: 2012-01-12 11:42:43
993Lagrangian Relaxation for Large-Scale Multi-Agent Planning Geoffrey J. Gordon† , Pradeep Varakantham‡ , William Yeoh∗ , Hoong Chuin Lau‡ , Ajay S. Aravamudhan‡ and Shih-Fen Cheng‡ † Machine Learning Departm

Lagrangian Relaxation for Large-Scale Multi-Agent Planning Geoffrey J. Gordon† , Pradeep Varakantham‡ , William Yeoh∗ , Hoong Chuin Lau‡ , Ajay S. Aravamudhan‡ and Shih-Fen Cheng‡ † Machine Learning Departm

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Source URL: www.cs.cmu.edu

Language: English - Date: 2013-03-07 09:00:10
994Abstract The primal-dual optimization algorithm developed in Chambolle and Pock (CP), 2011 is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm a

Abstract The primal-dual optimization algorithm developed in Chambolle and Pock (CP), 2011 is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm a

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Source URL: www.science-of-medicine.netne.net

Language: English - Date: 2013-02-01 20:13:19
995A practical dual gradient-projection method for large-scale, strictly-convex quadratic programming Nick Gould STFC Rutherford Appleton Laboratory with

A practical dual gradient-projection method for large-scale, strictly-convex quadratic programming Nick Gould STFC Rutherford Appleton Laboratory with

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Source URL: www.mims.manchester.ac.uk

Language: English - Date: 2013-10-24 05:07:09
996Layering As Optimization Decomposition Mung Chiang∗, Steven H. Low †, A. Robert Calderbank‡, John C. Doyle§ April 2, 2006 Abstract Network protocols in layered architectures have historically been obtained on an a

Layering As Optimization Decomposition Mung Chiang∗, Steven H. Low †, A. Robert Calderbank‡, John C. Doyle§ April 2, 2006 Abstract Network protocols in layered architectures have historically been obtained on an a

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Source URL: netlab.caltech.edu

Language: English - Date: 2006-07-28 18:20:34
997IEEE TRANS. ON CONTROL OF NETWORK SYSTEMS, JUNE[removed]WITH PROOFS) 1 Convex Relaxation of Optimal Power Flow Part II: Exactness

IEEE TRANS. ON CONTROL OF NETWORK SYSTEMS, JUNE[removed]WITH PROOFS) 1 Convex Relaxation of Optimal Power Flow Part II: Exactness

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Source URL: netlab.caltech.edu

Language: English - Date: 2014-05-06 00:32:56
998Optimization Algorithms in Machine Learning Stephen Wright University of Wisconsin-Madison NIPS Tutorial, 6 Dec 2010

Optimization Algorithms in Machine Learning Stephen Wright University of Wisconsin-Madison NIPS Tutorial, 6 Dec 2010

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Source URL: pages.cs.wisc.edu

Language: English - Date: 2010-12-08 23:07:50
999Dual Averaging Method for Regularized Stochastic Learning and Online Optimization Lin Xiao Microsoft Research, Redmond, WA[removed]removed]

Dual Averaging Method for Regularized Stochastic Learning and Online Optimization Lin Xiao Microsoft Research, Redmond, WA[removed]removed]

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Source URL: machinelearning.wustl.edu

Language: English - Date: 2013-03-25 14:07:10
1000IEEE TRANS. ON CONTROL OF NETWORK SYSTEMS, 1(1):15–27, MARCH[removed]WITH PROOFS) 1 Convex Relaxation of Optimal Power Flow Part I: Formulations and Equivalence

IEEE TRANS. ON CONTROL OF NETWORK SYSTEMS, 1(1):15–27, MARCH[removed]WITH PROOFS) 1 Convex Relaxation of Optimal Power Flow Part I: Formulations and Equivalence

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Source URL: netlab.caltech.edu

Language: English - Date: 2014-05-06 00:32:33