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Convex analysis / Operations research / Convex optimization / Convex function / Lipschitz continuity / Computational complexity theory / Oracle machine / Stochastic gradient descent / PP / Mathematical optimization / Mathematical analysis / Mathematics


IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 58, NO. 5, MAY[removed]Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization
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Document Date: 2012-05-10 11:53:02

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Company

Neural Information Processing Systems / Oracle / Microsoft / /

Country

United States / /

Currency

USD / /

/

Facility

University of Texas / Queensland University of Technology / University of California / /

IndustryTerm

optimization algorithms / stochastic optimization algorithms / related algorithms / /

Organization

National Science Foundation / Department of Computer Science / Department of Statistics / University of California / Berkeley / Air Force office of Scientific Research / Queensland University of Technology / Brisbane / U.S. Securities and Exchange Commission / Department of Electrical Engineering and Computer Science / School of Mathematical Sciences / University of Texas / /

Person

Theorem / Peter L. Bartlett / Pradeep Ravikumar / Martin J. Wainwright / /

Position

Associate Editor for Shannon Theory / /

Product

Fano / Lemma 2 / /

ProvinceOrState

New York / California / /

PublishedMedium

IEEE TRANSACTIONS ON INFORMATION THEORY / /

Technology

Stochastic optimization algorithms / optimization algorithms / machine learning / Digital Object Identifier / closely related algorithms / /

URL

http /

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