@report {1616911,
	title = {Mind the Duality Gap: Logarithmic regret algorithms for online optimization},
	year = {2009},
	address = {NeurIPS Proceedings},
	abstract = {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 HazanKaKaAg06. 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.},
	url = {https://proceedings.neurips.cc/paper/2008/file/bd686fd640be98efaae0091fa301e613-Paper.pdf},
	author = {Sham M. Kakade and Shai Shalev-Shwartz}
}
