In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solution of factored Markov decision processes (fMDPs). The traditional approximate value iteration algorithm is modified in two ways. For one, the least-squares projection operator is modified so that it does not increase max-norm, and thus preserves convergence. The other modification is that we un...

Abstract This note considers the momentum method by Polyak and accelerated gradient Nesterov, both without line search but with fixed step length applied to strongly convex quadratic functions assuming that exact gradients are used appropriate upper lower bounds for extreme eigenvalues of Hessian matrix known. Simple 2-d-examples show Euclidean distance iterates optimal solution is non-monotone...

In a recent paper by Monteiro and Svaiter, a hybrid proximal extragradient (HPE) framework was used to study the iteration-complexity of a first-order (or, in the context of optimization, second-order) method for solving monotone nonlinear equations. The purpose of this paper is to extend this analysis to study a prox-type first-order method for monotone smooth variational inequalities and incl...

-In this paper, we propose iterative soft-input soft-output (SISO) multi-carrier detection (inter-carrier interference cancellation) and LDPC decoding of the OFDM system in the mobile channel. The proposed SISO multi-carrier detection and sum-product decoding of the LDPC-coded OFDM system can achieve better error rate performance than previous SISO multi-carrier detection and BCJR decoding of c...

In this paper, we study the efficiency of a Restarted SubGradient (RSG) method that periodically restarts the standard subgradient method (SG). We show that, when applied to a broad class of convex optimization problems, RSG method can find an ǫ-optimal solution with a low complexity than SG method. In particular, we first show that RSG can reduce the dependence of SG’s iteration complexity on ...