Statistics – Machine Learning
Scientific paper
2010-09-03
Statistics
Machine Learning
Scientific paper
Relative to the large literature on upper bounds on complexity of convex optimization, lesser attention has been paid to the fundamental hardness of these problems. Given the extensive use of convex optimization in machine learning and statistics, gaining an understanding of these complexity-theoretic issues is important. In this paper, we study the complexity of stochastic convex optimization in an oracle model of computation. We improve upon known results and obtain tight minimax complexity estimates for various function classes.
Agarwal Alekh
Bartlett Peter L.
Ravikumar Pradeep
Wainwright Martin J.
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