This paper presents a general framework for adapting any generative (model-based) clustering algorithm to provide balanced solutions, i.e., clusters of comparable sizes. Partitional, model-based clustering algorithms are viewed as an iterative two-step optimization process—iterative model re-estimation and sample re-assignment. Instead of a maximum-likelihood (ML) assignment, a balanceconstrain...

If an iterative deepening search (IDS) procedure has the property that solutions at a given iteration are also found at later iterations, it is possible to skip iterations without loss of correctness. We examine the conditions required for skipping to be worthwhile and give an algorithm for dynamically adapting the skipping to the behaviour of the search procedure. We consider the problem f wit...

The Kohn-Sham equation in first-principles density functional theory (DFT) calculations is a nonlinear eigenvalue problem. Solving the nonlinear eigenproblem is usually the most expensive part in DFT calculations. Sparse iterative diagonalization methods that compute explicit eigenvectors can quickly become prohibitive for large scale problems. The Chebyshevfiltered subspace iteration (CheFSI) ...

An efficient two-step approach is presented for designing twochannel perfect-reconstruction linear-phase FIR filter banks. The first step involves finding a good solution by using an iterative procedure. This iterative procedure is generated by properly modifying the Lagrange-Newton method proposed by Horng and Willson. In the second step, the resulting solution is then used as a good initial s...

Classical iterative methods for the solution of algebraic linear systems of equations proceed by solving at each step a simpler system of equations. When this system is itself solved by an (inner) iterative method, the global method is called a two-stage iterative method. If this process is repeated, then the resulting method is called a nested iterative method. We study the convergence of such...

The paper proposes a Bayesian approach to absolute phase (not simply modulo-2π) estimation in interferometric aperture radar (InSAR). The observation density is 2π-periodic and accounts for the interferometric pair decorrelation and the system noise; the a priori probability of the absolute phase is modeled by a compound Gauss Markov random field (CGMRF). To compute the absolute phase estimate ...

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In this paper, based on Ostrowski’s method, a new family of eighth-order methods for solving nonlinear equations is derived. In terms of computational cost, each iteration of these methods requires three evaluations of the function and one evaluation of its first derivative, so that their efficiency indices are 1.682, which is optimal according to Kung and Traub’s conjecture. Numerical comparis...

The idea behind Compressive Sensing(CS) is the reconstruction of sparse signals from very few samples, by means of solving a convex optimization problem. In this paper we propose a compressive sensing framework using the Two-Step Iterative Shrinkage/ Thresholding Algorithms(TwIST) for reconstructing speech signals. Further, we compare this framework with two other convex optimization algorithms...