246 research outputs found
Preconditioning complex symmetric linear systems
A new polynomial preconditioner for symmetric complex linear systems based on
Hermitian and skew-Hermitian splitting (HSS) for complex symmetric linear
systems is herein presented. It applies to Conjugate Orthogonal Conjugate
Gradient (COCG) or Conjugate Orthogonal Conjugate Residual (COCR) iterative
solvers and does not require any estimation of the spectrum of the coefficient
matrix. An upper bound of the condition number of the preconditioned linear
system is provided. Moreover, to reduce the computational cost, an inexact
variant based on incomplete Cholesky decomposition or orthogonal polynomials is
proposed. Numerical results show that the present preconditioner and its
inexact variant are efficient and robust solvers for this class of linear
systems. A stability analysis of the method completes the description of the
preconditioner.Comment: 26 pages, 4 figures, 4 table
Computation of sum of squares polynomials from data points
We propose an iterative algorithm for the numerical computation of sums of
squares of polynomials approximating given data at prescribed interpolation
points. The method is based on the definition of a convex functional
arising from the dualization of a quadratic regression over the Cholesky
factors of the sum of squares decomposition. In order to justify the
construction, the domain of , the boundary of the domain and the behavior at
infinity are analyzed in details. When the data interpolate a positive
univariate polynomial, we show that in the context of the Lukacs sum of squares
representation, is coercive and strictly convex which yields a unique
critical point and a corresponding decomposition in sum of squares. For
multivariate polynomials which admit a decomposition in sum of squares and up
to a small perturbation of size , is always
coercive and so it minimum yields an approximate decomposition in sum of
squares. Various unconstrained descent algorithms are proposed to minimize .
Numerical examples are provided, for univariate and bivariate polynomials
Towards optimal explicit time-stepping schemes for the gyrokinetic equations
The nonlinear gyrokinetic equations describe plasma turbulence in laboratory
and astrophysical plasmas. To solve these equations, massively parallel codes
have been developed and run on present-day supercomputers. This paper describes
measures to improve the efficiency of such computations, thereby making them
more realistic. Explicit Runge-Kutta schemes are considered to be well suited
for time-stepping. Although the numerical algorithms are often highly
optimized, performance can still be improved by a suitable choice of the
time-stepping scheme, based on spectral analysis of the underlying operator.
Here, an operator splitting technique is introduced to combine first-order
Runge-Kutta-Chebychev schemes for the collision term with fourth-order schemes
for the remaining terms. In the nonlinear regime, based on the observation of
eigenvalue shifts due to the (generalized) advection term, an
accurate and robust estimate for the nonlinear timestep is developed. The
presented techniques can reduce simulation times by factors of up to three in
realistic cases. This substantial speedup encourages the use of similar
timestep optimized explicit schemes not only for the gyrokinetic equation, but
also for other applications with comparable properties.Comment: 11 pages, 5 figures, accepted for publication in Computer Physics
Communication
Enforcing passivity of parameterized LTI macromodels via Hamiltonian-driven multivariate adaptive sampling
We present an algorithm for passivity verification and enforcement of multivariate macromodels whose state-space matrices depend in closed form on a set of external or design parameters. Uniform passivity throughout the parameter space is a fundamental requirement of parameterized macromodels of physically passive structures, that must be guaranteed during model generation. Otherwise, numerical instabilities may occur, due to the ability of non-passive models to generate energy. In this work, we propose the first available algorithm that, starting from a generic parameter-depedent state-space model, identifies the regions in the frequency-parameter space where the model behaves locally as a non-passive system. The approach we pursue is based on an adaptive sampling scheme in the parameter space, which iteratively constructs and perturbs the eigenvalue spectrum of suitable Skew-Hamiltonian/Hamiltonian (SHH) pencils, with the objective of identifying the regions where some of these eigenvalues become purely imaginary, thus pinpointing local passivity violations. The proposed scheme is able to detect all relevant violations. An outer iterative perturbation method is then applied to the model coefficients in order to remove such violations and achieve uniform passivity. Although a formal proof of global convergence is not available, the effectiveness of the proposed implementation of the passivity verification and enforcement schemes is demonstrated on several examples
A Perturbation Scheme for Passivity Verification and Enforcement of Parameterized Macromodels
This paper presents an algorithm for checking and enforcing passivity of
behavioral reduced-order macromodels of LTI systems, whose frequency-domain
(scattering) responses depend on external parameters. Such models, which are
typically extracted from sampled input-output responses obtained from numerical
solution of first-principle physical models, usually expressed as Partial
Differential Equations, prove extremely useful in design flows, since they
allow optimization, what-if or sensitivity analyses, and design centering.
Starting from an implicit parameterization of both poles and residues of the
model, as resulting from well-known model identification schemes based on the
Generalized Sanathanan-Koerner iteration, we construct a parameter-dependent
Skew-Hamiltonian/Hamiltonian matrix pencil. The iterative extraction of purely
imaginary eigenvalues ot fhe pencil, combined with an adaptive sampling scheme
in the parameter space, is able to identify all regions in the
frequency-parameter plane where local passivity violations occur. Then, a
singular value perturbation scheme is setup to iteratively correct the model
coefficients, until all local passivity violations are eliminated. The final
result is a corrected model, which is uniformly passive throughout the
parameter range. Several numerical examples denomstrate the effectiveness of
the proposed approach.Comment: Submitted to the IEEE Transactions on Components, Packaging and
Manufacturing Technology on 13-Apr-201
Aggregation-based aggressive coarsening with polynomial smoothing
This paper develops an algebraic multigrid preconditioner for the graph
Laplacian. The proposed approach uses aggressive coarsening based on the
aggregation framework in the setup phase and a polynomial smoother with
sufficiently large degree within a (nonlinear) Algebraic Multilevel Iteration
as a preconditioner to the flexible Conjugate Gradient iteration in the solve
phase. We show that by combining these techniques it is possible to design a
simple and scalable algorithm. Results of the algorithm applied to graph
Laplacian systems arising from the standard linear finite element
discretization of the scalar Poisson problem are reported
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