262 research outputs found
Banach space projections and Petrov-Galerkin estimates
We sharpen the classic a priori error estimate of Babuska for Petrov-Galerkin
methods on a Banach space. In particular, we do so by (i) introducing a new
constant, called the Banach-Mazur constant, to describe the geometry of a
normed vector space; (ii) showing that, for a nontrivial projection , it is
possible to use the Banach-Mazur constant to improve upon the naive estimate ; and (iii) applying that improved estimate to
the Petrov-Galerkin projection operator. This generalizes and extends a 2003
result of Xu and Zikatanov for the special case of Hilbert spaces.Comment: 9 pages; v2: added new section on application to Lp and Sobolev
space
Discretization of Linear Problems in Banach Spaces: Residual Minimization, Nonlinear Petrov-Galerkin, and Monotone Mixed Methods
This work presents a comprehensive discretization theory for abstract linear
operator equations in Banach spaces. The fundamental starting point of the
theory is the idea of residual minimization in dual norms, and its inexact
version using discrete dual norms. It is shown that this development, in the
case of strictly-convex reflexive Banach spaces with strictly-convex dual,
gives rise to a class of nonlinear Petrov-Galerkin methods and, equivalently,
abstract mixed methods with monotone nonlinearity. Crucial in the formulation
of these methods is the (nonlinear) bijective duality map.
Under the Fortin condition, we prove discrete stability of the abstract
inexact method, and subsequently carry out a complete error analysis. As part
of our analysis, we prove new bounds for best-approximation projectors, which
involve constants depending on the geometry of the underlying Banach space. The
theory generalizes and extends the classical Petrov-Galerkin method as well as
existing residual-minimization approaches, such as the discontinuous
Petrov-Galerkin method.Comment: 43 pages, 2 figure
First order least squares method with weakly imposed boundary condition for convection dominated diffusion problems
We present and analyze a first order least squares method for convection
dominated diffusion problems, which provides robust L2 a priori error estimate
for the scalar variable even if the given data f in L2 space. The novel
theoretical approach is to rewrite the method in the framework of discontinuous
Petrov - Galerkin (DPG) method, and then show numerical stability by using a
key equation discovered by J. Gopalakrishnan and W. Qiu [Math. Comp. 83(2014),
pp. 537-552]. This new approach gives an alternative way to do numerical
analysis for least squares methods for a large class of differential equations.
We also show that the condition number of the global matrix is independent of
the diffusion coefficient. A key feature of the method is that there is no
stabilization parameter chosen empirically. In addition, Dirichlet boundary
condition is weakly imposed. Numerical experiments verify our theoretical
results and, in particular, show our way of weakly imposing Dirichlet boundary
condition is essential to the design of least squares methods - numerical
solutions on subdomains away from interior layers or boundary layers have
remarkable accuracy even on coarse meshes, which are unstructured
quasi-uniform
Compressive sensing Petrov-Galerkin approximation of high-dimensional parametric operator equations
We analyze the convergence of compressive sensing based sampling techniques
for the efficient evaluation of functionals of solutions for a class of
high-dimensional, affine-parametric, linear operator equations which depend on
possibly infinitely many parameters. The proposed algorithms are based on
so-called "non-intrusive" sampling of the high-dimensional parameter space,
reminiscent of Monte-Carlo sampling. In contrast to Monte-Carlo, however, a
functional of the parametric solution is then computed via compressive sensing
methods from samples of functionals of the solution. A key ingredient in our
analysis of independent interest consists in a generalization of recent results
on the approximate sparsity of generalized polynomial chaos representations
(gpc) of the parametric solution families, in terms of the gpc series with
respect to tensorized Chebyshev polynomials. In particular, we establish
sufficient conditions on the parametric inputs to the parametric operator
equation such that the Chebyshev coefficients of the gpc expansion are
contained in certain weighted -spaces for . Based on this we
show that reconstructions of the parametric solutions computed from the sampled
problems converge, with high probability, at the , resp.
convergence rates afforded by best -term approximations of the parametric
solution up to logarithmic factors.Comment: revised version, 27 page
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