We describe two nonconventional algorithms for linear regression, called GAME and CLASH. The salient characteristics of these approaches is that they exploit the convex `1-ball and non-convex `0-sparsity constraints jointly in sparse recovery. To establish the theoretical approximation guarantees of GAME and CLASH, we cover an interesting range of topics from game theory, convex and combinatori...

We propose an alternative method for computing e¤ectively the solution of the control inventory problem under non-convex polynomial cost functions. We apply the method of moments in global optimization to transform the corresponding, non-convex dynamic programming problem into an equivalent optimal control problem with linear and convex structure. We device computational tools based on convex o...

Many classical algorithms are found until several years later to outlive the confines in which they were conceived, and continue to be relevant in unforeseen settings. In this paper, we show that SVRG is one such method: being originally designed for strongly convex objectives, it is also very robust in non-strongly convex or sum-of-non-convex settings. More precisely, we provide new analysis t...

Most visibility culling algorithms require convexity of occluders. Occluder synthesis algorithms attempt to construct large convex occluders inside bulky non-convex sets. Occluder fusion algorithms generate convex occluders that are contained in the umbra cast by a group of objects given an area light. In this paper we prove that convexity requirements can be shifted from the occluders to their...

We present a simple algorithm to compute a convex decomposition of a non-convex, non-manifold polyhedron of arbitrary genus (handles). The algorithm takes a non-convex polyhedron with n edges and r notches (features causing non-convexity in the polyhedra) and produces a worst-case optimal O(r2 ) number of convex polyhedra Si, with U;S; = S, in O(nr2 ) time and O(nr) space. Recenlly, Chazelle an...

Given a nonconvex function f(x) that is an average of n smooth functions, we design stochastic first-order methods to find its approximate stationary points. The performance of our new methods depend on the smallest (negative) eigenvalue −σ of the Hessian. This parameter σ captures how strongly nonconvex f(x) is, and is analogous to the strong convexity parameter for convex optimization. At lea...