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Operations research / Convex optimization / Linear programming / Convex analysis / Semidefinite programming / Positive-definite matrix / Frank–Wolfe algorithm / Nonlinear dimensionality reduction / Cholesky decomposition / Mathematical optimization / Mathematics / Algebra


A Hybrid Algorithm for Convex Semidefinite Optimization S¨ oren Laue Friedrich-Schiller-University Jena, Germany
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Document Date: 2012-06-07 13:19:44

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Andrew Y. / Edinburgh / /

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SIAM Journal / Xing / Russell / /

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Germany / Jordan / United Kingdom / Scotland / /

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Facility

University Jena / /

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projected gradient descent algorithm / Sparse approximate solutions / less accurate solutions / highaccuracy solutions / sparse solutions / sublinear-time randomized approximation algorithm / nonlinear programming algorithm / hybrid algorithm / given solution / finite optimal solution / type algorithms / approximation algorithms / /

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U.S. Securities and Exchange Commission / Organization of the Paper Section / /

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Michael I. Learning / Michael I. Random / Williamson / Georgiana Dinu / Jos F. Using / Kenneth L. Coresets / Joachim Giesen / Lawrence K. Graph / David P. Improved / Sha / Laurent El / /

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author / /

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Machine Learning / Journal of Machine Learning Research / /

Technology

David P. Improved approximation algorithms / projected gradient descent algorithm / Frank-Wolfe algorithm / randomized algorithm / hybrid algorithm / nonlinear programming algorithm / DNA Chip / limited memory BFGS algorithm / Function Value Both algorithms / 1 Hybrid Algorithm / http / ISP / quasi-Newton algorithm / machine learning / sublinear-time randomized approximation algorithm / iterations Algorithm / DSPCA algorithm / html / FrankWolfe type algorithms / Hybrid Algorithm Our algorithm / Gui / /

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