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This paper presents a macromodeling technique for modeling distributed circuits characterized by noisy frequency-domain data. The proposed method is based on an iterative Loewner matrix (LM) approach. Using the LM approximation of previous iterations, the singular values and orthonormal matrices of the Loewner pencil are made to be more accurate. This approach is shown to minimize the biasing effect of the noisy data due to the nonlinearity of the singular value decomposition operation, resulting in more accurate poles and residues. Numerical examples illustrate that for noisy frequency-domain data, the proposed algorithm creates more accurate macromodels when compared with the traditional LM approach. [ABSTRACT FROM AUTHOR]

In this paper, we present a new low-complexity iterative joint phase estimation and decoding algorithm which can be applied to low-density-parity-check (or turbo) codes for channels affected by strong phase noise. The algorithm exhibits a very good performance and a very low complexity even in strong phase noise, high code rate, and high-order constellations. The proposed algorithm works by first obtaining a preliminary pilots-aided phase estimation. Next, the expressions for the belief propagation messages are approximated around that reference and reduced to a Gaussian canonical form. This results in a simple closed form of a recursive update. Simulations results for phase-shift-keying modulation of size 8 and quadrature-amplitude modulation of size 64 are presented and compared with other algorithms in the literature. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to study the order of convergence and the efficiency of four families of iterative methods using frozen divided differences. The first two families correspond to a generalization of the secant method and the implementation made by Schmidt and Schwetlick. The other two frozen schemes consist of a generalization of Kurchatov method and an improvement of this method applying the technique used by Schmidt and Schwetlick previously. An approximation of the local convergence order is generated by the examples, and it numerically confirms that the order of the methods is well deduced. Moreover, the computational efficiency indexes of the four algorithms are presented and computed in order to compare their efficiency. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose an iterative algorithm to solve large size linear inverse ill posed problems. The regularization problem is formulated as a constrained optimization problem. The dual Lagrangian problem is iteratively solved to compute an approximate solution. Before starting the iterations, the algorithm computes the necessary smoothing parameters and the error tolerances from the data. The numerical experiments performed on test problems show that the algorithm gives good results both in terms of precision and computational efficiency. [Copyright &y& Elsevier]

This paper presents a study of sequential parameter estimation based on a linear non-Gaussian observation model. To develop robust algorithms, we consider a family of heavy-tailed distributions that can be expressed as the scale mixture of Gaussian and extend the development to include some robust penalty functions. We treat the problem as a Bayesian learning problem and develop an iterative algorithm by using the Laplace approximation for the posterior and the minorization-maximization (MM) algorithm as an optimization tool. We then study a one-step implementation of the iterative algorithm. This leads to a family of generalized robust RLS-type of algorithms which include several well-known algorithms as special cases. Using a further simplification that the covariance is fixed, leads to a family of generalized robust LMS-type of algorithms. Through mathematical analysis and simulations, we demonstrate the robustness of these algorithms [ABSTRACT FROM PUBLISHER]

Many optimization problems reduce to the solution of a system of linear inequalities (SLI). Some solution methods use relaxed, averaged projections. Others invoke surrogate constraints (typically stemming from aggregation). This paper proposes a blend of these two approaches. A novelty comes from introducing as surrogate constraint a halfspace defined by differences of algorithmic iterates. The first iteration is identical to surrogate constraints methods. In next iterations, for a given approximation x¯, besides the violated constraints in x¯, we also take into consideration the surrogate inequality, which we have obtained in the previous iteration. The motivation for this research comes from the recent work of Scolnik et al. (Appl. Numer. Math. 41, 499—513, 2002), who studied some projection methods for a system of linear equations. [ABSTRACT FROM AUTHOR]