Approximate linear programming (ALP) is an efficient approach to solving large factored Markov decision processes (MDPs). The main idea of the method is to approximate the optimal...
System-level design presents special simulation modeling challenges. System-level models address the architectural and functional performance of complex systems. Systems are decom...
Gunar Schorcht, Ian A. Troxel, Keyvan Farhangian, ...
A new language and inference algorithm for stochastic modeling is presented. This work refines and generalizes the stochastic functional language originally proposed by [1]. The l...
Abstract. We consider the minimization of a smooth convex function regularized by the mixture of prior models. This problem is generally difficult to solve even each simpler regula...
Junzhou Huang, Shaoting Zhang, Dimitris N. Metaxas
In this paper, we first propose product Toeplitz preconditioners (in an inverse form) for non-Hermitian Toeplitz matrices generated by functions with zeros. Our inverse product-ty...