Howard’s policy iteration algorithm is one of the most widely used algorithms for finding optimal policies for controlling Markov Decision Processes (MDPs). When applied to weighted directed graphs, which may be viewed as Deterministic MDPs (DMDPs), Howard’s algorithm can be used to find Minimum Mean-Cost cycles (MMCC). Experimental studies suggest that Howard’s algorithm works extremely well i...

PURPOSE To accelerate the motion-compensated iterative reconstruction of multishot non-Cartesian diffusion data. METHOD The motion-compensated recovery of multishot non-Cartesian diffusion data is often performed using a modified iterative sensitivity-encoded algorithm. Specifically, the encoding matrix is replaced with a combination of nonuniform Fourier transforms and composite sensitivity ...

in this paper, we have proposed a new iterative method for finding the solution of ordinary differential equations of the first order. in this method we have extended the idea of variational iteration method by changing the general lagrange multiplier which is defined in the context of the variational iteration method.this causes the convergent rate of the method increased compared with the var...

In order to describe conditional iteration in quantum systems, we consider categories where hom-sets have a partial summation based on an axiomatisation of uniform convergence. Such structures, similar to Haghverdi’s Unique Decomposition Categories (UDCs), allow for a number of fundamental constructions including the standard, or ‘particle-style’, categorical trace. We demonstrate that the cate...

Conventional design patterns found in many pattern atalogues are stati omponents of reusable design knowledge. They are fully des riptive of the problems they will solve, but the des riptive knowledge and design they provide does not des ribe how they an work with other patterns in a design and development pro ess. Therefore, the ontention of this thesis is that the knowledge ontained within st...

We consider optimization problems with an objective function that is the sum of two convex terms: one is smooth and given by a black-box oracle, and the other is general but with a simple, known structure. We first present an accelerated proximal gradient (APG) method for problems where the smooth part of the objective function is also strongly convex. This method incorporates an efficient line...

This paper proposes two proximal Newton-CG methods for convex nonsmooth optimization problems in composite form. The algorithms are based on a a reformulation of the original nonsmooth problem as the unconstrained minimization of a continuously differentiable function, namely the forward-backward envelope (FBE). The first algorithm is based on a standard line search strategy, whereas the second...

This paper introduces an efficient modified derivative-free method for bound constrained optimization problems. It is based on the coordinate search method. During the running of the algorithm, it incorporates the progressive obtained local information into the current iteration. Actually, after we find two different suitable descent directions, we introduce the composite expansion step. By doi...

Although partially observable Markov decision processes (POMDPs) have shown great promise as a framework for dialog management in spoken dialog systems, important scalability issues remain. This paper tackles the problem of scaling slot-filling POMDP-based dialog managers to many slots with a novel technique called composite point-based value iteration (CSPBVI). CSPBVI creates a “local” POMDP p...

In this paper, we propose a stochastic Primal-Dual Hybrid Gradient (PDHG) approach for solving a wide spectrum of regularized stochastic minimization problems, where the regularization term is composite with a linear function. It has been recognized that solving this kind of problem is challenging since the closed-form solution of the proximal mapping associated with the regularization term is ...