In the long-term evolution (LTE) system, channel equalization makes compensation to restore the original signal, the paper puts forward iteration sphere decoding algorithm which combines traditional sphere decoding and the improved QR based on the conventional QR decomposition detection algorithm. It can effectively reduce the system complexity. At the same time, in QPSK and 16QAM, the simulati...

We study the convergence properties of an alternating proximal minimization algorithm for nonconvex structured functions of the type: L(x, y) = f(x)+Q(x, y)+g(y), where f : Rn → R∪{+∞} and g : Rm → R∪{+∞} are proper lower semicontinuous functions, and Q : Rn × Rm → R is a smooth C function which couples the variables x and y. The algorithm can be viewed as a proximal regularization of the usual...

begin{abstract} in this paper, we introduce an iterative method for amenable semigroup of non expansive mappings and infinite family of non expansive mappings in the frame work of hilbert spaces. we prove the strong convergence of the proposed iterative algorithm to the unique solution of a variational inequality, which is the optimality condition for a minimization problem. the results present...

begin{abstract} in this paper, we introduce an iterative method for amenable semigroup of non expansive mappings and infinite family of non expansive mappings in the frame work of hilbert spaces. we prove the strong convergence of the proposed iterative algorithm to the unique solution of a variational inequality, which is the optimality condition for a minimization problem. the results present...

1 Las Vegas and Monte Carlo algorithms Definition 1.1 A Las Vegas algorithm!Las Vegas algorithm is a randomized algorithms that always return the correct result. The only variant is that it's running time might change between executions. An example for a Las Vegas algorithm is the QuickSort algorithm. Definition 1.2 Monte Carlo algorithm!Monte Carlo algorithm is a randomized algorithm that migh...

Another question of interest concerns the behaviour of a learning algorithm in the infinite sample limit: as it receives more and more data, does the algorithm converge to an optimal prediction rule, i.e. does the generalization error of the learned function approach the optimal error? Recall that for a distribution D on X × Y and a loss ` : Y × Y→[0,∞), the optimal error w.r.t. D and ` is the ...

In this paper, optimal placement of Phasor Measurement Unit (PMU) using Global Positioning System (GPS) is discussed. Ant Colony Optimization (ACO), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for this problem. Pheromone evaporation coefficient and the probability of moving from state x to state y by ant are introduced into the ACO. The modifi...

It is well-known that many generalized integrals can be expressed by the Beta function B(x, y). In this paper, some relations between the generalized integrals and partial derivatives Bp,q(x, y) of the Beta function B(x, y) are given. Moreover, an algorithm for computing B(x, y) and Bp,q(x, y) has been developed. Finally, numerical examples show that the algorithm can be applied to compute some...

1 Estimation 1.1 EM algorithm We derive the EM algorithm for Uks withGamma( k; ) distributions, so S = Pp k=1 Uk isGamma( ; ) with = Pp k=1 k andW = U=S is Dirichlet( 1; : : : ; p), and S andW are independent. With a slight abuse of notation we are going to identify xB = ft1; : : : ; tmg with (m; t) from now on. The joint density of (m; t) and the latent variables (y; s;w) is f(m; t;y; s;w) = f...