This work explores approximate methods to solve Markov decision processes for large systems through Policy iteration.Two methods, one using an embedded discrete time Markov chain and the other using time scale separation, are defined and compared with the solution obtained using traditional Policy iteration. First step solutions are found and compared for a radio resource management problem wit...

In this paper, planar parametric Hermite cubic interpolants with small curvature variation are studied. By minimization of an appropriate approximate functional, it is shown that a unique solution of the interpolation problem exists, and has a nice geometric interpretation. The best solution of such a problem is a quadratic geometric interpolant. The optimal approximation order 4 of the solutio...

In this article, an approximate analytical solution of flow and heat transfer for a viscoelastic fluid in an axisymmetric channel with porous wall is presented. The solution is obtained through the use of a powerful method known as Optimal Homotopy Asymptotic Method (OHAM). We obtained the approximate analytical solution for dimensionless velocity and temperature for various parameters. The inf...

In this paper, by considering the variational iteration method, a kind of explicit exact and numerical solutions to the Lienard equation is obtained, and the numerical solutions has been compared with their known theoretical solution. The variational iteration method is based on Lagrange multipliers for identification of optimal value of parameters in a functional. Using this method, it is poss...

In this paper, we propose two simple yet efficient computational algorithms to obtain approximate optimal designs for multi-dimensional linear regression on a large variety of design spaces. We focus the commonly used criteria, D- and A-optimal criteria. For D-optimality, provide an alternative proof monotonic convergence D-optimal criterion algorithm design. further show that proposed converge...

Abstract. Given a compact parameter set Y ⊂ Rp, we consider polynomial optimization problems (Py) on Rn whose description depends on the parameter y ∈ Y. We assume that one can compute all moments of some probability measure φ on Y, absolutely continuous with respect to the Lebesgue measure (e.g. Y is a box or a simplex and φ is uniformly distributed). We then provide a hierarchy of semidefinit...