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Dive into the research topics where Dmitriy Leykekhman is active.

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Featured researches published by Dmitriy Leykekhman.


SIAM Journal on Numerical Analysis | 2010

Local Error Estimates for SUPG Solutions of Advection-Dominated Elliptic Linear-Quadratic Optimal Control Problems

Matthias Heinkenschloss; Dmitriy Leykekhman

We derive local error estimates for the discretization of optimal control problems governed by linear advection-diffusion partial differential equations (PDEs) using the streamline upwind/Petrov Galerkin (SUPG) stabilized finite element method. We show that if the SUPG method is used to solve optimization problems governed by an advection-dominated PDE, the convergence properties of the SUPG method is substantially different from the convergence properties of the SUPG method applied for the solution of an advection-dominated PDE. The reason is that the solution of the optimal control problem involves another advection-dominated PDE, the so-called adjoint equation, whose advection field is just the negative of the advection of the governing PDEs. For the solution of the optimal control problem, a coupled system involving both the original governing PDE as well as the adjoint PDE must be solved. We show that in the presence of a boundary layer, the local error between the solution of the SUPG discretized optimal control problem and the solution of the infinite dimensional problem is only of first order even if the error is computed locally in a region away from the boundary layer. In the presence of interior layers, we prove optimal convergence rates for the local error in a region away from the layer between the solution of the SUPG discretized optimal control problems and the solution of the infinite dimensional problem. Numerical examples are presented to illustrate some of the theoretical results.


Numerische Mathematik | 2009

Hölder estimates for Green’s functions on convex polyhedral domains and their applications to finite element methods

Johnny Guzmán; Dmitriy Leykekhman; Jürgen Rossmann; Alfred H. Schatz

A model second-order elliptic equation on a general convex polyhedral domain in three dimensions is considered. The aim of this paper is twofold: First sharp Hölder estimates for the corresponding Green’s function are obtained. As an applications of these estimates to finite element methods, we show the best approximation property of the error in


SIAM Journal on Numerical Analysis | 2012

Local Error Analysis of Discontinuous Galerkin Methods for Advection-Dominated Elliptic Linear-Quadratic Optimal Control Problems

Dmitriy Leykekhman; Matthias Heinkenschloss


Journal of Scientific Computing | 2012

Investigation of Commutative Properties of Discontinuous Galerkin Methods in PDE Constrained Optimal Control Problems

Dmitriy Leykekhman

{W^1_{\infty}}


Mathematics of Computation | 2011

Best approximation property in the ¹_{∞} norm for finite element methods on graded meshes

Alan Demlow; Dmitriy Leykekhman; Alfred H. Schatz; Lars B. Wahlbin


SIAM Journal on Numerical Analysis | 2013

Optimal A Priori Error Estimates of Parabolic Optimal Control Problems with Pointwise Control

Dmitriy Leykekhman; Boris Vexler

. In contrast to previously known results,


Computational Optimization and Applications | 2013

Optimal error estimates for finite element discretization of elliptic optimal control problems with finitely many pointwise state constraints

Dmitriy Leykekhman; Dominik Meidner; Boris Vexler


Mathematics of Computation | 2012

Pointwise error estimates of finite element approximations to the Stokes problem on convex polyhedra.

Johnny Guzmán; Dmitriy Leykekhman

{W_p^{2}}


Numerische Mathematik | 2004

Pointwise localized error estimates for parabolic finite element equations

Dmitriy Leykekhman


SIAM Journal on Numerical Analysis | 2016

Finite Element Pointwise Results on Convex Polyhedral Domains

Dmitriy Leykekhman; Boris Vexler

regularity for p > 3, which does not hold for general convex polyhedral domains, is not required. Furthermore, the new Green’s function estimates allow us to obtain localized error estimates at a point.

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Alan Demlow

University of Kentucky

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Michael Neilan

University of Pittsburgh

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Michael Pruitt

University of Connecticut

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Buyang Li

Hong Kong Polytechnic University

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