We introduce a limited memory version, L-FCFW, of the Fully-Corrective Frank-Wolfe ( FCFW) method with approximate correction, to solve the dual problem.

Neural Information Processing Systems http://nips.cc/

In addition, there is also a performance objective to optimize, i.e. a reward to be maximized, or

The present work investigates a generalization of previous contributions in the analysis of data frames. Our contributions can be summarized as follows.

In this paper, we address the graph matching problem.

An increasing array of learning and training tasks reduce to optimization problem in very large dimensions. The state-of-the-art algorithms in this regime are based on randomized coordinate descent (RCD) . V arious acceleration strategies were proposed for RCD in the literature in recent years, based on techniques such as Nesterov's momentum [

Existing approaches rely on various local heuristics for enforcing the acyclicity constraint.

Sampling and learning these symmetric DPP's is pretty well understood. In this work, we consider a new class of DPP's, which we call signed DPP's, where we break the symmetry and allow attractive behaviors.

We are also able to establish the accelerated linear rate for a function which is both strongly-convex and smooth.

This paper proposes a stochastic variant of a classic algorithm--the cubic-regularized Newton method [Nesterov and Polyak, 2006].