30,687 research outputs found
A note on the error analysis of classical Gram-Schmidt
An error analysis result is given for classical Gram--Schmidt factorization
of a full rank matrix into where is left orthogonal (has
orthonormal columns) and is upper triangular. The work presented here shows
that the computed satisfies \normal{R}=\normal{A}+E where is an
appropriately small backward error, but only if the diagonals of are
computed in a manner similar to Cholesky factorization of the normal equations
matrix.
A similar result is stated in [Giraud at al, Numer. Math.
101(1):87--100,2005]. However, for that result to hold, the diagonals of
must be computed in the manner recommended in this work.Comment: 12 pages This v2. v1 (from 2006) has not the biliographical reference
set (at all). This is the only modification between v1 and v2. If you want to
quote this paper, please quote the version published in Numerische Mathemati
Basic principles of hp Virtual Elements on quasiuniform meshes
In the present paper we initiate the study of Virtual Elements. We focus
on the case with uniform polynomial degree across the mesh and derive
theoretical convergence estimates that are explicit both in the mesh size
and in the polynomial degree in the case of finite Sobolev regularity.
Exponential convergence is proved in the case of analytic solutions. The
theoretical convergence results are validated in numerical experiments.
Finally, an initial study on the possible choice of local basis functions is
included
Certified lattice reduction
Quadratic form reduction and lattice reduction are fundamental tools in
computational number theory and in computer science, especially in
cryptography. The celebrated Lenstra-Lenstra-Lov\'asz reduction algorithm
(so-called LLL) has been improved in many ways through the past decades and
remains one of the central methods used for reducing integral lattice basis. In
particular, its floating-point variants-where the rational arithmetic required
by Gram-Schmidt orthogonalization is replaced by floating-point arithmetic-are
now the fastest known. However, the systematic study of the reduction theory of
real quadratic forms or, more generally, of real lattices is not widely
represented in the literature. When the problem arises, the lattice is usually
replaced by an integral approximation of (a multiple of) the original lattice,
which is then reduced. While practically useful and proven in some special
cases, this method doesn't offer any guarantee of success in general. In this
work, we present an adaptive-precision version of a generalized LLL algorithm
that covers this case in all generality. In particular, we replace
floating-point arithmetic by Interval Arithmetic to certify the behavior of the
algorithm. We conclude by giving a typical application of the result in
algebraic number theory for the reduction of ideal lattices in number fields.Comment: 23 page
Evaluating the Impact of SDC on the GMRES Iterative Solver
Increasing parallelism and transistor density, along with increasingly
tighter energy and peak power constraints, may force exposure of occasionally
incorrect computation or storage to application codes. Silent data corruption
(SDC) will likely be infrequent, yet one SDC suffices to make numerical
algorithms like iterative linear solvers cease progress towards the correct
answer. Thus, we focus on resilience of the iterative linear solver GMRES to a
single transient SDC. We derive inexpensive checks to detect the effects of an
SDC in GMRES that work for a more general SDC model than presuming a bit flip.
Our experiments show that when GMRES is used as the inner solver of an
inner-outer iteration, it can "run through" SDC of almost any magnitude in the
computationally intensive orthogonalization phase. That is, it gets the right
answer using faulty data without any required roll back. Those SDCs which it
cannot run through, get caught by our detection scheme
Structure Preserving Model Reduction of Parametric Hamiltonian Systems
While reduced-order models (ROMs) have been popular for efficiently solving
large systems of differential equations, the stability of reduced models over
long-time integration is of present challenges. We present a greedy approach
for ROM generation of parametric Hamiltonian systems that captures the
symplectic structure of Hamiltonian systems to ensure stability of the reduced
model. Through the greedy selection of basis vectors, two new vectors are added
at each iteration to the linear vector space to increase the accuracy of the
reduced basis. We use the error in the Hamiltonian due to model reduction as an
error indicator to search the parameter space and identify the next best basis
vectors. Under natural assumptions on the set of all solutions of the
Hamiltonian system under variation of the parameters, we show that the greedy
algorithm converges with exponential rate. Moreover, we demonstrate that
combining the greedy basis with the discrete empirical interpolation method
also preserves the symplectic structure. This enables the reduction of the
computational cost for nonlinear Hamiltonian systems. The efficiency, accuracy,
and stability of this model reduction technique is illustrated through
simulations of the parametric wave equation and the parametric Schrodinger
equation
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