2,813 research outputs found
Constructive updating/downdating of oblique projectors: a generalization of the Gram-Schmidt process
A generalization of the Gram-Schmidt procedure is achieved by providing
equations for updating and downdating oblique projectors. The work is motivated
by the problem of adaptive signal representation outside the orthogonal basis
setting. The proposed techniques are shown to be relevant to the problem of
discriminating signals produced by different phenomena when the order of the
signal model needs to be adjusted.Comment: As it will appear in Journal of Physics A: Mathematical and
Theoretical (2007
Short-recurrence Krylov subspace methods for the overlap Dirac operator at nonzero chemical potential
The overlap operator in lattice QCD requires the computation of the sign
function of a matrix, which is non-Hermitian in the presence of a quark
chemical potential. In previous work we introduced an Arnoldi-based Krylov
subspace approximation, which uses long recurrences. Even after the deflation
of critical eigenvalues, the low efficiency of the method restricts its
application to small lattices. Here we propose new short-recurrence methods
which strongly enhance the efficiency of the computational method. Using
rational approximations to the sign function we introduce two variants, based
on the restarted Arnoldi process and on the two-sided Lanczos method,
respectively, which become very efficient when combined with multishift
solvers. Alternatively, in the variant based on the two-sided Lanczos method
the sign function can be evaluated directly. We present numerical results which
compare the efficiencies of a restarted Arnoldi-based method and the direct
two-sided Lanczos approximation for various lattice sizes. We also show that
our new methods gain substantially when combined with deflation.Comment: 14 pages, 4 figures; as published in Comput. Phys. Commun., modified
data in Figs. 2,3 and 4 for improved implementation of FOM algorithm,
extended discussion of the algorithmic cos
Nonlinear non-extensive approach for identification of structured information
The problem of separating structured information representing phenomena of
differing natures is considered. A structure is assumed to be independent of
the others if can be represented in a complementary subspace. When the
concomitant subspaces are well separated the problem is readily solvable by a
linear technique. Otherwise, the linear approach fails to correctly
discriminate the required information. Hence, a non extensive approach is
proposed. The resulting nonlinear technique is shown to be suitable for dealing
with cases that cannot be tackled by the linear one.Comment: Physica A, in pres
Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Dynamics Prediction and Reconstruction with Oblique Projections
In this paper, we propose a probabilistic reduced-dimensional vector
autoregressive (PredVAR) model with oblique projections. This model partitions
the measurement space into a dynamic subspace and a static subspace that do not
need to be orthogonal. The partition allows us to apply an oblique projection
to extract dynamic latent variables (DLVs) from high-dimensional data with
maximized predictability. We develop an alternating iterative PredVAR algorithm
that exploits the interaction between updating the latent VAR dynamics and
estimating the oblique projection, using expectation maximization (EM) and a
statistical constraint. In addition, the noise covariance matrices are
estimated as a natural outcome of the EM method. A simulation case study of the
nonlinear Lorenz oscillation system illustrates the advantages of the proposed
approach over two alternatives
Restarted Hessenberg method for solving shifted nonsymmetric linear systems
It is known that the restarted full orthogonalization method (FOM)
outperforms the restarted generalized minimum residual (GMRES) method in
several circumstances for solving shifted linear systems when the shifts are
handled simultaneously. Many variants of them have been proposed to enhance
their performance. We show that another restarted method, the restarted
Hessenberg method [M. Heyouni, M\'ethode de Hessenberg G\'en\'eralis\'ee et
Applications, Ph.D. Thesis, Universit\'e des Sciences et Technologies de Lille,
France, 1996] based on Hessenberg procedure, can effectively be employed, which
can provide accelerating convergence rate with respect to the number of
restarts. Theoretical analysis shows that the new residual of shifted restarted
Hessenberg method is still collinear with each other. In these cases where the
proposed algorithm needs less enough CPU time elapsed to converge than the
earlier established restarted shifted FOM, weighted restarted shifted FOM, and
some other popular shifted iterative solvers based on the short-term vector
recurrence, as shown via extensive numerical experiments involving the recent
popular applications of handling the time fractional differential equations.Comment: 19 pages, 7 tables. Some corrections for updating the reference
Blind Source Separation with Compressively Sensed Linear Mixtures
This work studies the problem of simultaneously separating and reconstructing
signals from compressively sensed linear mixtures. We assume that all source
signals share a common sparse representation basis. The approach combines
classical Compressive Sensing (CS) theory with a linear mixing model. It allows
the mixtures to be sampled independently of each other. If samples are acquired
in the time domain, this means that the sensors need not be synchronized. Since
Blind Source Separation (BSS) from a linear mixture is only possible up to
permutation and scaling, factoring out these ambiguities leads to a
minimization problem on the so-called oblique manifold. We develop a geometric
conjugate subgradient method that scales to large systems for solving the
problem. Numerical results demonstrate the promising performance of the
proposed algorithm compared to several state of the art methods.Comment: 9 pages, 2 figure
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