Positive semidefinite

Results: 26



#Item
11Errata for Learning output kernels with block coordinate descent  Differently from what claimed in Lemma 4.1 of [1], the update for L does not preserve symmetry. However, this doesn’t compromise the validity of Algorit

Errata for Learning output kernels with block coordinate descent Differently from what claimed in Lemma 4.1 of [1], the update for L does not preserve symmetry. However, this doesn’t compromise the validity of Algorit

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Source URL: people.tuebingen.mpg.de

Language: English - Date: 2011-07-26 04:13:11
12A Reformulation of Support Vector Machines for General Confidence Functions Yuhong Guo1 and Dale Schuurmans2 1  Department of Computer & Information Sciences

A Reformulation of Support Vector Machines for General Confidence Functions Yuhong Guo1 and Dale Schuurmans2 1 Department of Computer & Information Sciences

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Source URL: webdocs.cs.ualberta.ca

Language: English - Date: 2009-08-10 11:58:06
13Mathematical Programming manuscript No. (will be inserted by the editor) Pablo A. Parrilo  Semidefinite programming relaxations for semialgebraic

Mathematical Programming manuscript No. (will be inserted by the editor) Pablo A. Parrilo Semidefinite programming relaxations for semialgebraic

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Source URL: cba.mit.edu

Language: English - Date: 2011-12-13 18:50:13
14Introduction Applications of Sd+ -valued affine processes in finance Characterization of affine processes on Sd+ Implications for financial modeling  Affine processes on positive semidefinite matrices

Introduction Applications of Sd+ -valued affine processes in finance Characterization of affine processes on Sd+ Implications for financial modeling Affine processes on positive semidefinite matrices

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Source URL: www.fields.utoronto.ca

Language: English - Date: 2010-06-20 11:41:57
15Computing the Nearest Correlation Matrix—A Problem from Finance Nicholas J. Higham[removed]MIMS EPrint: [removed]

Computing the Nearest Correlation Matrix—A Problem from Finance Nicholas J. Higham[removed]MIMS EPrint: [removed]

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Source URL: eprints.ma.man.ac.uk

Language: English - Date: 2006-06-01 12:02:18
16A  Proofs Theorem 1. For a positive semidefinite matrix L and x ∈ [0, 1]N , XY Y

A Proofs Theorem 1. For a positive semidefinite matrix L and x ∈ [0, 1]N , XY Y

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Source URL: homes.cs.washington.edu

Language: English - Date: 2013-04-28 17:52:02
17Control and Verification of High-Dimensional Systems with DSOS and SDSOS Programming Anirudha Majumdar, Amir Ali Ahmadi, and Russ Tedrake Abstract— In this paper, we consider linear programming (LP) and second order co

Control and Verification of High-Dimensional Systems with DSOS and SDSOS Programming Anirudha Majumdar, Amir Ali Ahmadi, and Russ Tedrake Abstract— In this paper, we consider linear programming (LP) and second order co

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Source URL: groups.csail.mit.edu

Language: English - Date: 2014-09-20 22:37:52
18PIMS Distinguished Lecture Series  Roger Horn University of Utah April 27, 2009 2:00 p.m.

PIMS Distinguished Lecture Series Roger Horn University of Utah April 27, 2009 2:00 p.m.

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Source URL: www.uregina.ca

Language: English - Date: 2014-03-12 16:17:21
19A QCQP Approach to Triangulation Chris Aholt1 , Sameer Agarwal2 , and Rekha Thomas1 1 University of Washington 2

A QCQP Approach to Triangulation Chris Aholt1 , Sameer Agarwal2 , and Rekha Thomas1 1 University of Washington 2

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Source URL: homes.cs.washington.edu

Language: English - Date: 2012-07-31 15:03:32
20A Hybrid Algorithm for Convex Semidefinite Optimization  S¨ oren Laue Friedrich-Schiller-University Jena, Germany

A Hybrid Algorithm for Convex Semidefinite Optimization S¨ oren Laue Friedrich-Schiller-University Jena, Germany

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Source URL: icml.cc

Language: English - Date: 2012-06-07 13:19:44