For the finite volume discretization of a second-order elliptic model problem, we derive a posteriori error estimates which take into account an inexact solution of the associated linear algebraic system. We show that the algebraic error can be bounded by constructing an equilibrated Raviart–Thomas–Nédélec discrete vector field whose divergence is given by a proper weighting of the residual vec...

By using inherent stopping criteria of LDPC codes, the average number of iterations can be substantially reduced at high signal to noise ratio (SNR). However, we encounter a problem when hybrid automatic repeat request (H-ARQ) systems are applied. Frequent failures of decoding at low SNR region imply that the decoder reaches the maximum number of iterations frequently and thus the decoding comp...

In this paper we derive a posteriori error estimates for the compositional model of multiphase Darcy flow in porous media, consisting of a system of strongly coupled nonlinear unsteady partial differential and algebraic equations. We show how to control the dual norm of the residual augmented by a nonconformity evaluation term by fully computable estimators. We then decompose the estimators int...

Context: Evolutionary algorithms have proved to be successful for generating test data for path coverage testing. However in this approach, the set of target paths to be covered may include some that are infeasible. It is impossible to find test data to cover those paths. Rather than searching indefinitely, or until a fixed limit of generations is reached, it would be desirable to stop searchin...

The lack of naturalness hampers the widespread application of speech synthesis. Increasing the size of the unit database in a concatenative speech synthesizer has been proposed as a method to increase the variety of units—thereby improving naturalness. However, expanding the unit database increases the computational cost of selecting the most appropriate unit and compounds the risk that a perce...

Minimum-variance unbiased estimates for linear regression models can be obtained by solving leastsquares problems. The conjugate gradient method can be successfully used in solving the symmetric and positive definite normal equations obtained from these least-squares problems. Taking into account the results of Golub and Meurant (1997, 2009) [10,11], Hestenes and Stiefel (1952) [17], and Strako...

The practical application of stochastic approximation methods require a reliable means to stop the iterative process when the estimate is close to the optimal value or when further improvement of the estimate is doubtful. Conventional ideas on stopping stochastic algorithms employ probabilistic criteria based on the asymptotic distribution of the stochastic approximation process, often with the...

There is a natural norm associated with a starting point of the homogeneous selfdual (HSD) embedding model for conic convex optimization. In this norm two measures of the HSD model’s behavior are precisely controlled independent of the problem instance: (i) the sizes of ε-optimal solutions, and (ii) the maximum distance of ε-optimal solutions to the boundary of the cone of the HSD variables. Th...

We introduce a new iterative regularization procedure for inverse problems based on the use of Bregman distances, with particular focus on problems arising in image processing. We are motivated by the problem of restoring noisy and blurry images via variational methods, by using total variation regularization. We obtain rigorous convergence results, and effective stopping criteria for the gener...

In this article we develop and analyze novel iterative regularization techniques for the solution of systems of nonlinear ill–posed operator equations. The basic idea consists in considering separately each equation of this system and incorporating a loping strategy. The first technique is a Kaczmarz–type method, equipped with a novel stopping criteria. The second method is obtained using an em...