150 research outputs found
A parallel algorithm for solving linear parabolic evolution equations
We present an algorithm for the solution of a simultaneous space-time
discretization of linear parabolic evolution equations with a symmetric
differential operator in space. Building on earlier work, we recast this
discretization into a Schur-complement equation whose solution is a
quasi-optimal approximation to the weak solution of the equation at hand.
Choosing a tensor-product discretization, we arrive at a remarkably simple
linear system. Using wavelets in time and standard finite elements in space, we
solve the resulting system in linear complexity on a single processor, and in
polylogarithmic complexity when parallelized in both space and time. We
complement these theoretical findings with large-scale parallel computations
showing the effectiveness of the method
Fast and Efficient Numerical Methods for an Extended Black-Scholes Model
An efficient linear solver plays an important role while solving partial
differential equations (PDEs) and partial integro-differential equations
(PIDEs) type mathematical models. In most cases, the efficiency depends on the
stability and accuracy of the numerical scheme considered. In this article we
consider a PIDE that arises in option pricing theory (financial problems) as
well as in various scientific modeling and deal with two different topics. In
the first part of the article, we study several iterative techniques
(preconditioned) for the PIDE model. A wavelet basis and a Fourier sine basis
have been used to design various preconditioners to improve the convergence
criteria of iterative solvers. We implement a multigrid (MG) iterative method.
In fact, we approximate the problem using a finite difference scheme, then
implement a few preconditioned Krylov subspace methods as well as a MG method
to speed up the computation. Then, in the second part in this study, we analyze
the stability and the accuracy of two different one step schemes to approximate
the model.Comment: 29 pages; 10 figure
Three real-space discretization techniques in electronic structure calculations
A characteristic feature of the state-of-the-art of real-space methods in
electronic structure calculations is the diversity of the techniques used in
the discretization of the relevant partial differential equations. In this
context, the main approaches include finite-difference methods, various types
of finite-elements and wavelets. This paper reports on the results of several
code development projects that approach problems related to the electronic
structure using these three different discretization methods. We review the
ideas behind these methods, give examples of their applications, and discuss
their similarities and differences.Comment: 39 pages, 10 figures, accepted to a special issue of "physica status
solidi (b) - basic solid state physics" devoted to the CECAM workshop "State
of the art developments and perspectives of real-space electronic structure
techniques in condensed matter and molecular physics". v2: Minor stylistic
and typographical changes, partly inspired by referee comment
Recommended from our members
Mini-Workshop: Adaptive Methods for Control Problems Constrained by Time-Dependent PDEs
Optimization problems constrained by time-dependent PDEs (Partial Differential Equations) are challenging from a computational point of view: even in the simplest case, one needs to solve a system of PDEs coupled globally in time and space for the unknown solutions (the state, the costate and the control of the system). Typical and practically relevant examples are the control of nonlinear heat equations as they appear in laser hardening or the thermic control of flow problems (Boussinesq equations). Specifically for PDEs with a long time horizon, conventional time-stepping methods require an enormous storage of the respective other variables. In contrast, adaptive methods aim at distributing the available degrees of freedom in an a-posteriori-fashion to capture singularities and are, therefore, most promising
Time-parallel iterative solvers for parabolic evolution equations
We present original time-parallel algorithms for the solution of the implicit
Euler discretization of general linear parabolic evolution equations with
time-dependent self-adjoint spatial operators. Motivated by the inf-sup theory
of parabolic problems, we show that the standard nonsymmetric time-global
system can be equivalently reformulated as an original symmetric saddle-point
system that remains inf-sup stable with respect to the same natural parabolic
norms. We then propose and analyse an efficient and readily implementable
parallel-in-time preconditioner to be used with an inexact Uzawa method. The
proposed preconditioner is non-intrusive and easy to implement in practice, and
also features the key theoretical advantages of robust spectral bounds, leading
to convergence rates that are independent of the number of time-steps, final
time, or spatial mesh sizes, and also a theoretical parallel complexity that
grows only logarithmically with respect to the number of time-steps. Numerical
experiments with large-scale parallel computations show the effectiveness of
the method, along with its good weak and strong scaling properties
Fast Solvers for Cahn-Hilliard Inpainting
We consider the efficient solution of the modified Cahn-Hilliard equation for binary image inpainting using convexity splitting, which allows an unconditionally gradient stable time-discretization scheme. We look at a double-well as well as a double obstacle potential. For the latter we get a nonlinear system for which we apply a semi-smooth Newton method combined with a Moreau-Yosida regularization technique. At the heart of both methods lies the solution of large and sparse linear systems. We introduce and study block-triangular preconditioners using an efficient and easy to apply Schur complement approximation. Numerical results indicate that our preconditioners work very well for both problems and show that qualitatively better results can be obtained using the double obstacle potential
- …