We address the problem of minimizing a convex function over the space of large matrices with low rank. While this optimization problem is hard in general, we propose an efficient...
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 bound...
We introduce a framework for computing statistically optimal estimates of geometric reconstruction problems. While traditional algorithms often suffer from either local minima or ...
As an important geometric property of many structures or structural components, convexity plays an important role in computer vision and image understanding. In this paper, we desc...
Song Wang, Joachim S. Stahl, Adam Bailey, Michael ...
We consider the problem of two-dimensional outputsensitive convex hull in the cache-oblivious model. That is, we are interested in minimizing the number of cache faults caused whe...