Markov random fields are designed to represent structured dependencies among large collections of random variables, and are well-suited to capture the structure of real-world sign...
Tanya Roosta, Martin J. Wainwright, Shankar S. Sas...
The fact that the heat equation is controllable to zero in any bounded domain of the euclidean space, any time T > 0 and from any open subset of the boundary is well known. On ...
A wide variety of stability and performance questions about linear dynamical systems can be reformulated as convex optimization problems involving linear matrix inequalities (LMIs...
Erin M. Aylward, Pablo A. Parrilo, Jean-Jacques E....
We consider the class of nonlinear optimal control problems (OCP) with polynomial data, i.e., the differential equation, state and control constraints and cost are all described by...
Jean B. Lasserre, Didier Henrion, Christophe Prieu...
We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise, a problem closely related to several cryptograph...