Quasi-Newton method

Results: 39



#Item
11Black-box optimization benchmarking of the GLOBAL method L´aszlo´ P´al  Faculty of Economic and Human Sciences, Sapientia – Hungarian University of

Black-box optimization benchmarking of the GLOBAL method L´aszlo´ P´al Faculty of Economic and Human Sciences, Sapientia – Hungarian University of

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Source URL: www.mat.univie.ac.at

Language: English - Date: 2012-07-04 06:19:05
12New Quasi-Newton Methods for Efficient Large-Scale Machine Learning S.V. N. Vishwanathan Joint work with Nic Schraudolph, Simon Günter, Jin Yu, Peter Sunehag, and Jochen Trumpf National ICT Australia and Australian Nati

New Quasi-Newton Methods for Efficient Large-Scale Machine Learning S.V. N. Vishwanathan Joint work with Nic Schraudolph, Simon Günter, Jin Yu, Peter Sunehag, and Jochen Trumpf National ICT Australia and Australian Nati

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Source URL: www.stat.purdue.edu

Language: English - Date: 2009-08-21 20:36:33
13Journal of Machine Learning Research–57  Submitted 11/08; Revised 11/09; Published -/10 A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning

Journal of Machine Learning Research–57 Submitted 11/08; Revised 11/09; Published -/10 A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning

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Source URL: www.stat.purdue.edu

Language: English - Date: 2010-03-07 22:04:30
14Advanced Review  Geometry optimization H. Bernhard Schlegel∗ Geometry optimization is an important part of most quantum chemical calculations. This article surveys methods for optimizing equilibrium geometries, locatin

Advanced Review Geometry optimization H. Bernhard Schlegel∗ Geometry optimization is an important part of most quantum chemical calculations. This article surveys methods for optimizing equilibrium geometries, locatin

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Source URL: chem.wayne.edu

Language: English - Date: 2011-08-23 22:02:14
15GRAS SAF Report 03 Ref: SAF/GRAS/METO/REP/GSR/003 Web: www.grassaf.org Date: 20 NovemberGRAS SAF Report 03

GRAS SAF Report 03 Ref: SAF/GRAS/METO/REP/GSR/003 Web: www.grassaf.org Date: 20 NovemberGRAS SAF Report 03

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Source URL: www.romsaf.org

Language: English - Date: 2010-04-20 11:46:40
16MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Greedy Sparsity-Constrained Optimization  Bahmani, S.; Raj, B.; Boufounos, P.T.

MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Greedy Sparsity-Constrained Optimization Bahmani, S.; Raj, B.; Boufounos, P.T.

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Source URL: www.merl.com

Language: English - Date: 2013-07-15 07:04:10
17Ch.7 Nonlinear optimization [Book, Chap. 5] To appreciate the vast difference between linear optimization and nonlinear optimization, consider the relation y = w0 +  L

Ch.7 Nonlinear optimization [Book, Chap. 5] To appreciate the vast difference between linear optimization and nonlinear optimization, consider the relation y = w0 + L

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Source URL: www.ocgy.ubc.ca

Language: English - Date: 2013-10-28 03:07:27
18Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli Stanford University {jascha,benpoole,sganguli}@stanford.edu

Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli Stanford University {jascha,benpoole,sganguli}@stanford.edu

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Source URL: www.opt-ml.org

Language: English - Date: 2014-12-13 10:45:07
19AIAA JOURNAL Vol. 50, No. 5, May 2012 Provably Convergent Multifidelity Optimization Algorithm Not Requiring High-Fidelity Derivatives Andrew March∗ and Karen Willcox†

AIAA JOURNAL Vol. 50, No. 5, May 2012 Provably Convergent Multifidelity Optimization Algorithm Not Requiring High-Fidelity Derivatives Andrew March∗ and Karen Willcox†

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Source URL: raphael.mit.edu

Language: English - Date: 2013-06-19 14:19:05
20Package ‘nnls’ July 2, 2014 Type Package Title The Lawson-Hanson algorithm for non-negative least squares (NNLS) Version 1.4 Author Katharine M. Mullen and Ivo H. M. van Stokkum

Package ‘nnls’ July 2, 2014 Type Package Title The Lawson-Hanson algorithm for non-negative least squares (NNLS) Version 1.4 Author Katharine M. Mullen and Ivo H. M. van Stokkum

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Source URL: cran.r-project.org

Language: English - Date: 2014-07-02 15:52:16