We initiate the study of inverse problems in approximate uniform generation, focusing on uniform generation of satisfying assignments of various types of Boolean functions. In such an inverse problem, the algorithm is given uniform random satisfying assignments of an unknown function f belonging to a class C of Boolean functions (such as linear threshold functions or polynomial-size DNF formula...

In this paper we propose an algorithm for polynomial-time reinforcement learning in factored Markov decision processes (FMDPs). The factored optimistic initial model (FOIM) algorithm, maintains an empirical model of the FMDP in a conventional way, and always follows a greedy policy with respect to its model. The only trick of the algorithm is that the model is initialized optimistically. We pro...

Hybrid approximate linear programming (HALP) has recently emerged as a promising framework for solving large factored Markov decision processes (MDPs) with discrete and continuous state and action variables. Our work addresses its major computational bottleneck – constraint satisfaction in large structured domains of discrete and continuous variables. We analyze this problem and propose a novel...

This article considers the backward error of the solution of polynomial eigenvalue problems expressed as Lagrange interpolants. One of the most common strategies to solve polynomial eigenvalue problems is to linearize, which is to say that the polynomial eigenvalue problem is transformed into an equivalent larger linear eigenvalue problem, and solved using any appropriate eigensolver. Much of t...

Both the forward and backward kinematics of the Gough-Stewart mechanism exhibit nonlinear behavior. It is critically important to take account of this nonlinearity in some applications such as path control in parallel kinematics machine tools. The nonlinearity of inverse kinematics is straightforward and has been first studied in this paper. However the nonlinearity of forward kinematics is mor...

analysis of time series data can involve the inversion of large covariance matrices. for theclass of arma (p, q) processes there are no exact explicit expressions for these inverses, except for thema (1) process. in practice, the sample covariance matrix can be very large and inversion can becomputationally time consuming and so approximate explicit expressions for the inverse are desirable.thi...

INTRODUCTION. Patients with posterior cruciate ligament (PCL) deficient sometimes experience giving way during stair descending, although they do not usually have disability in performing most of activities of daily living (ADL). This fact suggests that the patients should have adaptation mechanism to lack of posterior stability during the activity. However the mechanics of PCL deficient knee d...

The matrix-oriented version of the conjugate gradient (CG) method can be used to approximate solution certain linear matrix equations. To limit memory consumption, low-rank reduction factored iterates is often employed, possibly leading disruption regular convergence behavior. We analyze properties in regime and identify quantities that are responsible for early termination, usually stagnation,...