This paper analyzes the complexity of on-line reinforcement learning algorithms, namely asynchronous realtime versions of Q-learning and value-iteration, applied to the problem of...
This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood a...
In the finite element analysis that deals with large deformation, the process usually produces distorted elements at the later stages of the analysis. These distorted elements lea...
—This paper presents an auxiliary model based stochastic gradient parameter estimation algorithm for multiinput output-error systems by minimizing a quadratic cost function. The ...
Abstract--This paper considers the noncooperative maximization of mutual information in the vector Gaussian interference channel in a fully distributed fashion via game theory. Thi...