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2008

Mind the Duality Gap: Logarithmic regret algorithms for online optimization

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Mind the Duality Gap: Logarithmic regret algorithms for online optimization
We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms. Our framework yields the tightest known logarithmic regret bounds for Follow-The-Leader and for the gradient descent algorithm proposed in Hazan et al. [2006]. We then show that one can interpolate between these two extreme cases. In particular, we derive a new algorithm that shares the computational simplicity of gradient descent but achieves lower regret in many practical situations. Finally, we further extend our framework for generalized strongly convex functions.
Shai Shalev-Shwartz, Sham M. Kakade
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Shai Shalev-Shwartz, Sham M. Kakade
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