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A new method is proposed to solve an ellipsoid-constrained integer least squares (EILS) problem arising in communications. In this method, the LLL reduction, which is cast as a QRZ factorization of a matrix, is used to transform the original EILS problem to a reduced EILS problem, and then a search algorithm is proposed to solve the reduced EILS problem. Simulation results indicate the new method can be much more computationally efficient than the existing method. The method is extended to solve a more general EILS problem. [ABSTRACT FROM AUTHOR]

The article traces the history and evolution of Netlib and NA-Net, two resources which emerged in the 1980s to serve the needs of the scientific computing community. The authors also examine the continuing impact of these resources in the computing community. Netlib is software repository initially established by Bell Labs' Eric Grosse and Argonne National Laboratory's Jack Dongarra in 1984 that facilitates easy and efficient distribution of public-domain computational software. On the other hand, NA-Net, traces its origin to a list of email addresses maintained and distributed by Stanford University's Gene Golub, to others within the numerical analysis community, which eventually supported an online directory and newsletter digest.

Abstract: Generalized eigenvalue problems play a significant role in many applications. In this paper, continuous methods are presented to compute generalized eigenvalues and their corresponding eigenvectors for two real symmetric matrices. Our study only requires that the right-hand-side matrix is positive semi-definite. The main idea of our continuous methods is to convert the generalized eigenvalue problem into an optimization problem. Then a continuous method which includes both a merit function and an ordinary differential equation (ODE) is introduced for the resulting optimization problem. The strong convergence of the ODE solution is proved for any starting point. Both the generalized eigenvalues and their corresponding eigenvectors can be easily obtained under some mild conditions. Some numerical results are also presented. [Copyright &y& Elsevier]

Abstract: We study theoretical properties of two inexact Hermitian/skew-Hermitian splitting (IHSS) iteration methods for the large sparse non-Hermitian positive definite system of linear equations. In the inner iteration processes, we employ the conjugate gradient (CG) method to solve the linear systems associated with the Hermitian part, and the Lanczos or conjugate gradient for normal equations (CGNE) method to solve the linear systems associated with the skew-Hermitian part, respectively, resulting in IHSS(CG, Lanczos) and IHSS(CG, CGNE) iteration methods, correspondingly. Theoretical analyses show that both IHSS(CG, Lanczos) and IHSS(CG, CGNE) converge unconditionally to the exact solution of the non-Hermitian positive definite linear system. Moreover, their contraction factors and asymptotic convergence rates are dominantly dependent on the spectrum of the Hermitian part, but are less dependent on the spectrum of the skew-Hermitian part, and are independent of the eigenvectors of the matrices involved. Optimal choices of the inner iteration steps in the IHSS(CG, Lanczos) and IHSS(CG, CGNE) iterations are discussed in detail by considering both global convergence speed and overall computation workload, and computational efficiencies of both inexact iterations are analyzed and compared deliberately. [Copyright &y& Elsevier]

We describe the use of a higher-order singular value decomposition (HOSVD) in transforming a data tensor of genes x "x-settings." that is, different settings of the experimental variable x x "y-settings," which tabulates DNA microarray data from different studies, to a "core tensor" of "eigenarrays" x "x-eigengenes" x "y-eigengenes." Reformulating this multilinear HOSVD such that it decomposes the data tensor into a linear superposition of all outer products of an eigenarray, an x- and a y-eigengene, that is, rank-1 "subtensors," we define the significance of each subtensor in terms of the fraction of the overall information in the data tensor that it captures. We illustrate this HOSVD with an integration of genome-scale mRNA expression data from three yeast cell cycle time courses, two of which are under exposure to either hydrogen peroxide or menadione. We find that significant subtensors represent independent biological programs or experimental phenomena. The picture that emerges suggests that the conserved genes YKU70, MRE11, AIF1, and ZWF1, and the processes of retrotransposition, apoptosis. and the oxidative pentose phosphate pathway that these genes are involved in, may play significant, yet previously unrecognized, roles in the differential effects of hydrogen peroxide and menadione on cell cycle progression. A genome-scale correlation between DNA replication initiation and RNA transcription, which is equivalent to a recently discovered correlation and might be due to a previously unknown mechanism of regulation, is independently uncovered. [ABSTRACT FROM AUTHOR]

One of the most powerful iterative schemes for solving symmetric, positive definite linear systems is the conjugate gradient algorithm of Hestenes and Stiefel [J. Res. Nat. Bur. Standards, 49 (1952), pp. 409-435], especially when it is combined with preconditioning (cf. [P. Concus, G.H. Golub, and D.P. O'Leary, in Proceedings of the Symposium on Sparse Matrix Computations, Argonne National Laboratory, 1975, Academic, New York, 1976]). In many applications, the solution of a sequence of equations with the same coefficient matrix is required. We propose an approach based on a combination of the conjugate gradient method with Chebyshev filtering polynomials, applied only to a part of the spectrum of the coefficient matrix, as preconditioners that target some specific convergence properties of the conjugate gradient method. We show that our preconditioner puts a large number of eigenvalues near one and do not degrade the distribution of the smallest ones. This procedure enables us to construct a lower dimensional Krylov basis that is very rich with respect to the smallest eigenvalues and associated eigenvectors. A major benefit of our method is that this information can then be exploited in a straightforward way to solve sequences of systems with little extra work. We illustrate the performance of our method through numerical experiments on a set of linear systems. [ABSTRACT FROM AUTHOR]

We further generalize the technique for constructing the Hermitian/skew-Hermitian splitting (HSS) iteration method for solving large sparse non-Hermitian positive definite system of linear equations to the normal/skew-Hermitian (NS) splitting obtaining a class of normal/skew-Hermitian splitting (NSS) iteration methods. Theoretical analyses show that the NSS method converges unconditionally to the exact solution of the system of linear equations. Moreover, we derive an upper bound of the contraction factor of the NSS iteration which is dependent solely on the spectrum of the normal splitting matrix, and is independent of the eigenvectors of the matrices involved. We present a successive-overrelaxation (SOR) acceleration scheme for the NSS iteration, which specifically results in an acceleration scheme for the HSS iteration. Convergence conditions for this SOR scheme are derived under the assumption that the eigenvalues of the corresponding block Jacobi iteration matrix lie in certain regions in the complex plane. A numerical example is used to show that the SOR technique can significantly accelerate the convergence rate of the NSS or the HSS iteration method. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We establish a class of accelerated Hermitian and skew-Hermitian splitting (AHSS) iteration methods for large sparse saddle-point problems by making use of the Hermitian and skew-Hermitian splitting (HSS) iteration technique. These methods involve two iteration parameters whose special choices can recover the known preconditioned HSS iteration methods, as well as yield new ones. Theoretical analyses show that the new methods converge unconditionally to the unique solution of the saddle-point problem. Moreover, the optimal choices of the iteration parameters involved and the corresponding asymptotic convergence rates of the new methods are computed exactly. In addition, theoretical properties of the preconditioned Krylov subspace methods such as GMRES are investigated in detail when the AHSS iterations are employed as their preconditioners. Numerical experiments confirm the correctness of the theory and the effectiveness of the methods. [ABSTRACT FROM AUTHOR]

The convergence features of a preconditioned algorithm for the convection-diffusion equation based on its diffusion part are considered. Analyses of the distribution of the eigenvalues of the preconditioned matrix in arbitrary dimensions and of the fundamental parameters of convergence are provided, showing the existence of a proper cluster of eigenvalues. The structure of the cluster is not influenced by the discretization. An upper bound on the condition number of the eigenvector matrix under some assumptions is provided as well. The overall cost of the algorithm is O(n), where n is the size of the underlying matrices. [ABSTRACT FROM AUTHOR]

The optimal parameter of the Hermitian/skew-Hermitian splitting (HSS) iteration method for a real two-by-two linear system is obtained. The result is used to determine the optimal parameters for linear systems associated with certain two-by-two block matrices and to estimate the optimal parameters of the HSS iteration method for linear systems with n-by-n real coefficient matrices. Numerical examples are given to illustrate the results. [ABSTRACT FROM AUTHOR]

We describe the singular value decomposition (SVD) of yeast genome-scale mRNA lengths distribution data measured by DNA microarrays. SVD uncovers in the mRNA abundance levels data matrix of genes x arrays, i.e., electrophoretic gel migration lengths or mRNA lengths, mathematically unique decorrelated and decoupled ‘eigengenes.’ The eigengenes are the eigenvectors of the arrays x arrays correlation matrix, with the corresponding series of eigenvalues proportional to the series of the ‘fractions of eigen abundance.’ Each fraction of eigen abundance indicates the significance of the corresponding eigengene relative to all others. We show that the eigengenes fit ‘asymmetric Hermite functions,’ a generalization of the eigenfunctions of the quantum harmonic oscillator and the integral transform which kernel is a generalized coherent state. The fractions of eigen abundance fit a geometric series as do the eigenvalues of the integral transform which kernel is a generalized coherent state. The ‘asymmetric generalized coherent state’ models the measured data, where the profiles of mRNA abundance levels of most genes as well as the distribution of the peaks of these profiles fit asymmetric Gaussians. We hypothesize that the asymmetry in the distribution of the peaks of the profiles is due to two competing evolutionary forces. We show that the asymmetry in the profiles of the genes might be due to a previously unknown asymmetry in the gel electrophoresis thermal broadening of a moving, rather than a stationary, band of RNA molecules. [ABSTRACT FROM AUTHOR]

In this paper a generalized eigenvalue problem for nonsquare pencils of the form A - λB with A,B ϵ Cmxn and m > n, which was proposed recently by Boutry, Elad, Golub, and Milanfar [SIAM J. Matrix Anal. Appl., 27 (2006), pp. 582–601], is studied. An algebraic characterization for the distance between the pair (A,B) and the pairs (A0,B0) with the property that for the pair (A0,B0) there exist l distinct eigenpairs of the form (A0-λkB0)v-k = 0, k = 1, … , l, is given, which implies that this distance can be obtained by solving an optimization problem over the compact set {Vl : Vl ϵ Cnxl, VH l VHlVl = I}. Furthermore, the distance between a controllable descriptor system and uncontrollable ones is also considered, an algebraic characterization is obtained, and hence a well-known result on the distance between a controllable linear time-invariant system to uncontrollable ones is extended to the descriptor systems. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, continuous methods are introduced to compute both the extreme and interior eigenvalues and their corresponding eigenvectors for real symmetric matrices. The main idea is to convert the extreme and interior eigenvalue problems into some optimization problems. Then a continuous method which includes both a merit function and an ordinary differential equation (ODE) is introduced for each resulting optimization problem. The convergence of each ODE solution is proved for any starting point. The limit of each ODE solution for any starting point is fully studied. Both the extreme and the interior eigenvalues and their corresponding eigenvectors can be easily obtained under a very mild condition. Promising numerical results are also presented. [Copyright &y& Elsevier]

For the iterative solution of saddle point problems, a nonsymmetric preconditioner is studied which, with respect to the upper-left block of the system matrix, can be seen as a variant of SSOR. An idealized situation where SSOR is taken with respect to the skew-symmetric part plus the diagonal part of the upper-left block is analyzed in detail. Since action of the preconditioner involves solution of a Schur complement system, an inexact form of the preconditioner can be of interest. This results in an inner-outer iterative process. Numerical experiments with solution of linearized Navier-Stokes equations demonstrate the efficiency of the new preconditioner, especially when the upper-left block is far from symmetric. [ABSTRACT FROM AUTHOR]

We describe the use of the matrix eigenvalue decomposition (EVD) and pseudoinverse projection and a tensor higher-order EVD (HOEVD) in reconstructing the pathways that compose a cellular system from genome-scale nondirectional networks of correlations among the genes of the system. The EVD formulates a genes x genes network as a linear superposition of genes × genes decorrelated and decoupled rank-I subnetworks, which can be associated with functionally independent pathways. The integrative pseudoinverse projection of a network computed from a "data" signal onto a designated "basis" signal approximates the network as a linear superposition of only the subnetworks that are common to both signals and simulates observation of only the pathways that are manifest in both experiments. We define a comparative HOEVD that formulates a series of networks as linear superpositions of decorrelated rank-1 subnetworks and the rank-2 couplings among these subnetworks, which can be associated with independent pathways and the transitions among them common to all networks in the series or exclusive to a subset of the networks. Boolean functions of the discretized subnetworks and couplings highlight differential, i.e., pathway-dependent relations among genes. We illustrate the EVD, pseudoinverse projection, and HO-EVD of genome-scale networks with analyses of yeast DNA microarray data. [ABSTRACT FROM AUTHOR]

We consider a positive definite block preconditioner for solving saddle point linear systems. An approach based on augmenting the (1,1) block while keeping its condition number small is described, and algebraic analysis is performed. Ways of selecting the parameters involved are discussed, and analytical and numerical observations are given. [ABSTRACT FROM AUTHOR]

We describe an integrative data-driven mathematical framework that formulates any number of genome-scale molecular biological data sets in terms of one chosen set of data samples, or of profiles extracted mathematically from data samples, designated the "basis" set. By using pseudoinverse projection, the molecular biological profiles of the data samples are least-squares-approximated as superositions of the basis profiles. Reconstruction of the data in the basis simulates experimental observation of only the cellular states manifest in the data that correspond to those of the basis. Classification of the data samples according to their reconstruction in the basis, rather than their overall measured profiles, maps the cellular states of the data onto those of the basis and gives a global picture of the correlations and possibly also causal coordination of these two sets of states. We illustrate this framework with an integration of yeast genome-scale proteins' DNA-binding data with cell cycle mRNA expression time course data. Novel correlation between DNA replication initiation and RNA transcription during the yeast cell cycle, which might be due to a previously unknown mechanism of regulation, is predicted. [ABSTRACT FROM AUTHOR]

In this paper we consider the solution of linear systems of saddle point type by preconditioned Krylov subspace methods. A preconditioning strategy based on the symmetric/skew-symmetric splitting of the coefficient matrix is proposed, and some useful properties of the preconditioned matrix are established. The potential of this approach is illustrated by numerical experiments with matrices from various application areas. [ABSTRACT FROM AUTHOR]

We consider 2 × 2 block indefinite linear systems whose (2, 2) block is zero. Such systems arise in many applications. We discuss two techniques that are based on modifying the (1, 1) block in a way that makes the system easier to solve. The main part of the paper focuses on an augmented Lagrangian approach: a technique that modifies the (1,1) block without changing the system size. The choice of the parameter involved, the spectrum of the linear system, and its condition number are discussed, and some analytical observations are provided. A technique of deflating the (1,1) block is then introduced. Finally, numerical experiments that validate the analysis are presented. [ABSTRACT FROM AUTHOR]

Examines efficient iterative methods for the large sparse non-Hermitian positive definite systems of linear equations based on the Hermitian and skew-Hermitian splitting of the coefficient matrix. Demonstration that the Hermitian/skew-Hermitian splitting iteration method converges unconditionally to the unique solution of the system of linear equations.

A new decomposition of a nonsingular matrix, the Symmetric times Triangular (ST) decomposition, is proposed. By this decomposition, every nonsingular matrix can be represented as a product of a symmetric matrix S and a triangular matrix T. Furthermore, S can be made positive definite. Two numerical algorithms for computing the ST decomposition with positive definite S are presented. [ABSTRACT FROM AUTHOR]

In this paper, we present an inverse free Krylov subspace method for finding some extreme eigenvalues of the symmetric definite generalized eigenvalue problem Ax = λBx. The basic method takes a form of inner-outer iterations and involves no inversion of B or any shift-and-invert matrix A - λ[sub 0]B. A convergence analysis is presented that leads to a preconditioning scheme for accelerating convergence through some equivalent transformations of the eigenvalue problem. Numerical examples are given to illustrate the convergence properties and to demonstrate the competitiveness of the method. [ABSTRACT FROM AUTHOR]

A parallel algorithm is proposed for the solution of narrow banded non-symmetric linear systems. The linear system is partitioned into blocks of rows with a small number of unknowns common to multiple blocks. Our technique yields a reduced system defined only on these common unknowns which can then be solved by a direct or iterative method. A projection based extension to this approach is also proposed for computing the reduced system implicitly, which gives rise to an inner–outer iteration method. In addition, the product of a vector with the reduced system matrix can be computed efficiently on a multiprocessor by concurrent projections onto subspaces of block rows. Scalable implementations of the algorithm can be devized for hierarchical parallel architectures by exploiting the two-level parallelism inherent in the method. Our experiments indicate that the proposed algorithm is a robust and competitive alternative to existing methods, particularly for difficult problems with strong indefinite symmetric part. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The singular value decomposition (SVD) has been extensively used in engineering and statistical applications. This method was originally discovered by Eckart and Young in [Psychometrika, 1 (1936), pp. 211-218], where they considered the problem of low-rank approximation to a matrix. A natural generalization of the SVD is the problem of low-rank approximation to high order tensors, which we call the multidimensional SVD. In this paper, we investigate certain properties of this decomposition as well as numerical algorithms. [ABSTRACT FROM AUTHOR]

Several SOR-like methods are proposed for solving augmented systems. These have many different applications inscientific computing, for example, constrained optimization and the finite element method for solving the Stokes equation. The convergence and the choice of optimal parameter for these algorithms are studied. The convergence and divergence regions for some algorithms are given, and the new algorithms are applied to solve the Stokes equations as well. [ABSTRACT FROM AUTHOR]

In this paper, we study an inexact inverse iteration with inner-outer iterations for solving the generalized eigenvalue problem Ax = λBx, and analyze how the accuracy in the inner iterations affects the convergence of the outer iterations. By considering a special stopping criterion depending on a threshold parameter, we show that the outer iteration converges linearly with the inner threshold parameter as the convergence rate. We also discuss the total amount of work and asymptotic equivalence between this stopping criterion and a more standard one. Numerical examples are given to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Preconditioners are often conceived as approximate inverses. For nonsingular indefinite matrices of saddle-point (or KKT) form, we show how preconditioners incorporating an exact Schur complement lead to preconditioned matrices with exactly two or exactly three distinct eigenvalues. Thus approximations of the Schur complement lead to preconditioners which can be very effective even though they are in no sense approximate inverses. [ABSTRACT FROM AUTHOR]

Let A be a symmetric matrix and let f be a smooth function defined on an interval containing the spectrum of A. Generalizing a well-known result of Demko, Moss and Smith on the decay of the inverse we show that when A is banded, the entries of ƒ(A) are bounded in an exponentially decaying manner away from the main diagonal. Bounds obtained by representing the entries of ƒ(A) in terms of Riemann-Stieltjes integrals and by approximating such integrals by Gaussian quadrature rules are also considered. Applications of these bounds to preconditioning are suggested and illustrated by a few numerical examples. [ABSTRACT FROM AUTHOR]

Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditioned coefficient matrix, and in order to compute stable solutions to these systems it is necessary to apply regularization methods. We show how Tikhonov's regularization method, which in its original formulation involves a least squares problem, can be recast in a total least squares formulation suited for problems in which both the coefficient matrix and the right-hand side are known only approximately. We analyze the regularizing properties of this method and demonstrate by a numerical example that, in certain cases with large perturbations, the new method is superior to standard regularization methods. [ABSTRACT FROM AUTHOR]

An important variation of preconditioned conjugate gradient algorithms is inexact preconditioner implemented with inner-outer iterations [G. H. Golub and M. L. Overton, Numerical Analysis, Lecture Notes in Math. 912, Springer, Berlin, New York, 1982], where the preconditioner is solved by an inner iteration to a prescribed precision. In this paper, we formulate an inexact preconditioned conjugate gradient algorithm for a symmetric positive definite system and analyze its convergence property. We establish a linear convergence result using a local relation of residual norms. We also analyze the algorithm using a global equation and show that the algorithm may have the superlinear convergence property when the inner iteration is solved to high accuracy. The analysis is in agreement with observed numerical behavior of the algorithm. In particular, it suggests a heuristic choice of the stopping threshold for the inner iteration. Numerical examples are given to show the effectiveness of this choice and to compare the convergence bound. [ABSTRACT FROM AUTHOR]

We derive a stable technique, based upon matrix pencils, for the reconstruction of (or approximation by) polygonal shapes from moments. We point out that this problem can be considered the dual of 2 — D numerical quadrature over polygonal domains. An analysis of the sensitivity of the problem is presented along with some numerical examples illustrating the relevant points. Finally, an application to the problem of gravimetry is explored where the shape of a gravitationally anomalous region is to be recovered from measurements of its exterior gravitational field. [ABSTRACT FROM AUTHOR]

We present a new method for solving total variation (TV) minimization problems in image restoration. The main idea is to remove some of the singularity caused by the nondifferentiability of the quantity ¦∇u¦ in the definition of the TV-norm before we apply a linearization technique such as Newton's method. This is accomplished by introducing an additional variable for the flux quantity appearing in the gradient of the objective function, which can be interpreted as the normal vector to the level sets of the image u. Our method can be viewed as a primal-dual method as proposed by Conn and Overton [A Primal-Dual Interior Point Method for Minimizing a Sum of Euclidean Norms, Preprint, 1994] and Andersen [Ph.D. thesis, Odense University, Denmark, 1995] for the minimization of a sum of Euclidean norms. In addition to possessing local quadratic convergence, experimental results show that the new method seems to be globally convergent.

Discusses cross validation's difficulty in evaluating function that requires the calculation of the trace of an inverse matrix. Solution of the least squares problem; How stochastic trace estimators demonstrates numerical approximation techniques reducing computational complexity; Utilization of Gauss quadrature; How factorization of large matrices can be avoided; Accuracy of stochastic trace estimator.

The OR algorithm is a basic algorithm for computing the eigenvalues of dense matrices. For efficiency reasons it is prerequisite that the algorithm is applied only after the original matrix has been reduced to a matrix of a particular shape, most notably Hessenberg and tridiagonal, which is preserved during the iterative process. In certain circumstances a reduction to another matrix shape may be advantageous. In this paper, we identify which zero patterns of symmetric matrices are preserved under the OR algorithm. [ABSTRACT FROM AUTHOR]

A method is developed that computes an exact nonorthogonal analysis of variance using cell means. The method is iterative and does not require that the nonorthogonal design matrix be stored or formed. At each stage in the process, a balanced analysis of variance problem must be solved. A monotonicity property for the estimates of the regression sum of squares is derived that could be used to minimize iteration in hypothesis testing. An application of the algorithm to the solution of analysis of covariance problems is also given. [ABSTRACT FROM AUTHOR]

We analyze the preconditioned Chebyshev iteration in which at each step the linear system involving the preconditioner is solved inexactly by an inner iteration. We allow the tolerance used in the inner iteration to decrease from one outer iteration to the next. When the tolerance converges to zero, the asymptotic convergence rate is the same as for the exact method. Motivated by this result, we seek the sequence of tolerance values that yields the lowest cost to achieve a specified accuracy. We find that among all sequences of slowly varying tolerances, a constant one is optimal. Numerical calculations that verify our results are presented. Asymptotic methods, such as the W.K.B. method for linear recurrence equations, are used with an estimate of the accuracy of the asymptotic result. [ABSTRACT FROM AUTHOR]

We consider a generic sequence of matrices (the nonnormal case is of interest) showing a proper cluster at zero in the sense of the singular values. By a direct use of the notion of majorizations, we show that the uniform spectral boundedness is sufficient for the proper clustering at zero of the eigenvalues: if the assumption of boundedness is removed, then we can construct sequences of matrices with a proper singular value clustering and having all the eigenvalues of an arbitrarily big modulus. Applications to the preconditioning theory are discussed. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of recovering a planar polygon from its measured complex moments. These moments correspond to an indicator function defined over the polygon's support. Previous work on this problem gave necessary and sufficient conditions for such successful recovery process and focused mainly on the case of exact measurements being given. In this paper, we extend these results and treat the same problem in the case where a longer than necessary series of noise corrupted moments is given. Similar to methods found in array processing, system identification, and signal processing, we discuss a set of possible estimation procedures that are based on the Prony and the Pencil methods, relate them one to the other, and compare them through simulations. We then present an improvement over these methods based on the direct use of the maximum-likelihood estimator, exploiting the above methods as initialization. Finally, we show how regularization and, thus, maximum a posteriori probability estimator could be applied to reflect prior knowledge about the recovered polygon. [ABSTRACT FROM AUTHOR]

Registration using the least-squares cost function is sensitive to the intensity fluctuations caused by the blood oxygen level dependent (BOLD) signal in functional MRI (fMRI) experiments, resulting in stimulus-correlated motion errors. These errors are severe enough to cause false-positive clusters in the activation maps of datasets acquired from 3T scanners. This paper presents a new approach to resolving the coupling between registration and activation. Instead of treating the two problems as individual steps in a sequence, they are combined into a single leastsquares problem and are solved simultaneously. Robustness tests on a variety of simulated three -- dimensional EPI datasets show that the stimulus-correlated motion errors are removed, resulting in a substantial decrease in false-positive and false-negative activation rates. The new method is also shown to decorrelate the motion estimates from the stimulus by testing it on different in vivo fMRI datasets acquired from two different 3T scanners. [ABSTRACT FROM AUTHOR]

This paper analyzes rank modification of symmetric positive definite matrices H of the form H - M + P, where H - M denotes a step of reducing H to a lower-rank, symmetric and positive semidefinite matrix and (H - M) + P denotes a step of restoring H - M to a sym- metric positive definite matrix. These steps and their generalizations for rectangular matrices are fully characterized. The well-known BFGS and DFP updates used in Hessian and inverse Hessian approximations provided the motivation for the generalizations and are special cases with H and P having rank one. [ABSTRACT FROM AUTHOR]

A variational formulation for the generalized singular value decomposition (GSVD) of a pair of matrices A ∈ Rm×n and B ∈ Rp×n is presented. In particular, a duality theory analogous to that of the SVD provides new understanding of left and right generalized singular vectors. It is shown that the intersection of row spaces of A and B plays a key role in the GSVD duality theory. The main result that characterizes left GSVD vectors involves a generalized singular value deflation process. [ABSTRACT FROM AUTHOR]

Recent work on block-based compression for low bit-rate coding has shown that employing a block coder within a sampling scheme where the image is downsampled prior to coding (and upsampled after the decoding stage) results in superior performance compared to standard block coding. In this paper, we explore the use of optimal decimation and interpolation filters in this coding scheme. We show that the problem of finding optimal filters for a general, unknown, "black-box" coder can be written as a separable least squares problem in two sets of variables. We then elegantly solve this optimization problem using the Variable Projection method. The experimental results presented dearly exhibit a significant improvement over existing approaches. [ABSTRACT FROM AUTHOR]