Past Computational Mathematics and Applications Seminar

The asymptotic rate of convergence of the trapezium rule is

defined, for smooth functions, by the Euler-Maclaurin expansion.

The extension to other methods, such as Gauss rules, is straightforward;

this talk begins with some special cases, such as Periodic functions, and

functions with various singularities.

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Convergence rates for ODEs (Initial and Boundary value problems)

and for PDEs are available, but not so well known. Extension to singular

problems seems to require methods specific to each situation. Some of

the results are unexpected - to me, anyway.

In the past few years we have developed some expertise in solving optimization

problems that involve large scale simulations in various areas of Computational

Geophysics and Engineering. We will discuss some of those applications here,

namely: inversion of seismic data, characterization of piezoelectrical crystals

material properties, optimal design of piezoelectrical transducers and

opto-electronic devices, and the optimal design of steel structures.

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A common theme among these different applications is that the goal functional

is very expensive to evaluate, often, no derivatives are readily available, and

some times the dimensionality can be large.

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Thus parallelism is a need, and when no derivatives are present, search type

methods have to be used for the optimization part. Additional difficulties can

be ill-conditioning and non-convexity, that leads to issues of global

optimization. Another area that has not been extensively explored in numerical

optimization and that is important in real applications is that of

multiobjective optimization.

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As a result of these varied experiences we are currently designing a toolbox

to facilitate the rapid deployment of these techniques to other areas of

application with a minimum of retooling.

A-Posteriori Error estimates for high order Godunov finite

volume methods are presented which exploit the two solution

representations inherent in the method, viz. as piecewise

constants $u_0$ and cell-wise $q$-th order reconstructed

functions $R^0_q u_0$. The analysis provided here applies

directly to schemes such as MUSCL, TVD, UNO, ENO, WENO or any

other scheme that is a faithful extension of Godunov's method

to high order accuracy in a sense that will be made precise.

Using standard duality arguments, we construct exact error

representation formulas for derived functionals that are

tailored to the class of high order Godunov finite volume

methods with data reconstruction, $R^0_q u_0$. We then consider

computable error estimates that exploit the structure of higher

order Godunov finite volume methods. The analysis technique used

in this work exploits a certain relationship between higher

order Godunov methods and the discontinuous Galerkin method.

Issues such as the treatment of nonlinearity and the optional

post-processing of numerical dual data are also discussed.

Numerical results for linear and nonlinear scalar conservation

laws are presented to verify the analysis. Complete details can

be found in a paper appearing in the proceedings of FVCA3,

Porquerolles, France, June 24-28, 2002.

SMP (Symmetric Multi-Processors) hardware technologies are very popular

with vendors and end-users alike for a number of reasons. However, true

shared memory parallelism has experienced somewhat slower to take up

amongst the scientific-programming community. NAG has been at the

forefront of SMP technology for a number of years, and the NAG SMP

Library has shown the potential of SMP systems.

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At the very high end, SMP hardware technologies are used as building

blocks of modern supercomputers, which truly consist of clusters of SMP

systems, for which no dedicated model of parallelism yet exists.

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The aim of this talk is to introduce SMP systems and their potential.

Results from our work at NAG will also be introduced to show how SMP

parallelism, based on a shared memory paradigm, can be used to very

good effect and can produce high performance, scalable software. The

talk also aims to discuss some aspects of the apparent slow take up of

shared memory parallelism and the potential competition from PC (i.e.

Intel)-based cluster technology. The talk then aims to explore the

potential of SMP technology within "hybrid parallelism", i.e. mixed

distributed and shared memory modes, illustrating the point with some

preliminary work carried out by the author and others. Finally, a

number of potential future challenges to numerical analysts will be

discussed.

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The talk is aimed at all who are interested in SMP technologies for

numerical computing, irrespective of any previous experience in the

field. The talk aims to stimulate discussion, by presenting some ideas,

backing these with data, not to stifle it in an ocean of detail!

It is well known that discrete solutions to the convection-diffusion

equation contain nonphysical oscillations when boundary layers are present

but not resolved by the discretisation. For the Galerkin finite element

method with linear elements on a uniform 1D grid, a precise statement as

to exactly when such oscillations occur can be made, namely, that for a

problem with mesh size h, constant advective velocity and different values

at the left and right boundaries, oscillations will occur if the mesh

P\'{e}clet number $P_e$ is greater than one. In 2D, however, the situation

is not so well understood. In this talk, we present an analysis of a 2D

model problem on a square domain with grid-aligned flow which enables us

to clarify precisely when oscillations occur, and what can be done to

prevent them. We prove the somewhat surprising result that there are

oscillations in the 2D problem even when $P_e$ is less than 1. Also, we show that there

are distinct effects arising from differences in the top and bottom

boundary conditions (equivalent to those seen in 1D), and the non-zero

boundaries parallel to the flow direction.

Algebra based modeling systems are becoming essential elements in the

application of large and complex mathematical programs. These systems

enable the abstraction, expression and translation of practical

problems into reliable and effective operational systems. They provide

the bridged between algorithms and real world problems by automating

the problem analysis and translation into specific data structures and

provide computational services required by different solvers. The

modeling system GAMS will be used to illustrate the design goals and

main features of such systems. Applications in use and under

development will be used to provide the context for discussing the

changes in user focus and future requirements. This presents new sets

of opportunities and challenges to the supplier and implementer of

mathematical programming solvers and modeling systems.

A systematic approach to error control and mesh adaptation for

optimal control of systems governed by PDEs is presented.

Starting from a coarse mesh, the finite element spaces are successively

enriched in order to construct suitable discrete models.

This process is guided by an a posteriori error estimator which employs

sensitivity factors from the adjoint equation.

We consider different examples with the stationary Navier-Stokes

equations as state equation.

When steady solutions of complex physical problems are computed numerically it is often crucial to compute their stability in order to, for example, check that the computed solution is "physical", or carry out a sensitivity analysis, or help understand complex nonlinear phenomena near a bifurcation point. Usually a stability analysis requires the solution of an eigenvalue problem which may arise in its own right or as an appropriate linearisation. In the case of discretized PDEs the corresponding matrix eigenvalue problem will often be of generalised form: \\ $Ax=\lambda Mx$ (1) \\ with $A$ and $M$ large and sparse. In general $A$ is unsymmetric and $M$ is positive semi-definite. Only a small number of "dangerous" eigenvalues are usually required, often those (possibly complex) eigenvalues nearest the imaginary axis. In this context it is usually necessary to perform "shift-invert" iterations, which require repeated solution of systems of the form \\ $(A - \sigma M)y = Mx$, (2) \\ for some shift $\sigma$ (which may be near a spectral point) and for various right-hand sides $x$. In large applications systems (2) have to be solved iteratively, requiring "inner iterations". \\ \\ In this talk we will describe recent progress in the construction, analysis and implementation of fast algorithms for finding such eigenvalues, utilising algebraic domain decomposition techniques for the inner iterations. \\ \\ In the first part we will describe an analysis of inverse iteration techniques for (1) for a model problem in the presence of errors arising from inexact solves of (2). The delicate interplay between the convergence of the (outer) inverse iteration and the choice of tolerance for the inner solves can be used to determine an efficient iterative method provided a good preconditioner for $A$ is available. \\ \\ In the second part we describe an application to the computation of bifurcations in Navier-Stokes problems discretised by mixed finite elements applied to the velocity-pressure formulation. We describe the construction of appropriate preconditioners for the corresponding (3 x 3 block) version of (2). These use additive Schwarz methods and can be applied to any unstructured mesh in 2D or 3D and for any selected elements. An important part of the preconditioner is the adaptive coarsening strategy. At the heart of this are recent extensions of the Bath domain decomposition code DOUG, carried out by Eero Vainikko. \\ \\ An application to the computation of a Hopf bifurcation of planar flow around a cylinder will be given. \\ \\ This is joint work with Jörg Berns-Müller, Andrew Cliffe, Alastair Spence and Eero Vainikko and is supported by EPSRC Grant GR/M59075.

In this presentation we examine the convergence characteristics of a

Krylov subspace solver preconditioned by a new indefinite

constraint-type preconditioner, when applied to discrete systems

arising from low-order mixed finite element approximation of the

classical biharmonic problem. The preconditioning operator leads to

preconditioned systems having an eigenvalue distribution consisting of

a tightly clustered set together with a small number of outliers. We

compare the convergence characteristics of a new approach with the

convergence characteristics of a standard block-diagonal Schur

complement preconditioner that has proved to be extremely effective in

the context of mixed approximation methods.

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In the second part of the presentation we are concerned with the

efficient parallel implementation of proposed algorithm on modern

shared memory architectures. We consider use of the efficient parallel

"black-box'' solvers for the Dirichlet Laplacian problems based on

sparse Cholesky factorisation and multigrid, and for this purpose we

use publicly available codes from the HSL library and MGNet collection.

We compare the performance of our algorithm with sparse direct solvers

from the HSL library and discuss some implementation related issues.

(Joint work with Felipe Cucker and Dennis Cheung, City University of Hong Kong.)

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Condition numbers are important complexity-theoretic tools to capture

a "distillation" of the input aspects of a computational problem that

determine the running time of algorithms for its solution and the

sensitivity of the computed output. The motivation for our work is the

desire to understand the average case behaviour of linear programming

algorithms for a large class of randomly generated input data in the

computational model of a machine that computes with real numbers. In

this model it is not known whether linear programming is polynomial

time solvable, or so-called "strongly polynomial". Closely related to

linear programming is the problem of either proving non-existence of

or finding an explicit example of a point in a polyhedral cone defined

in terms of certain input data. A natural condition number for this

computational problem was developed by Cheung and Cucker, and we analyse

its distributions under a rather general family of input distributions.

We distinguish random sampling of primal and dual constraints

respectively, two cases that necessitate completely different techniques

of analysis. We derive the exact exponents of the decay rates of the

distribution tails and prove various limit theorems of complexity

theoretic importance. An interesting result is that the existence of

the k-th moment of Cheung-Cucker's condition number depends only very

mildly on the distribution of the input data. Our results also form

the basis for a second paper in which we analyse the distributions of

Renegar's condition number for the randomly generated linear programming

problem.

Toeplitz matrices enjoy the dual virtues of ubiquity and beauty. We begin this talk by surveying some of the interesting spectral properties of such matrices, emphasizing the distinctions between infinite-dimensional Toeplitz matrices and the large-dimensional limit of the corresponding finite matrices. These basic results utilize the algebraic Toeplitz structure, but in many applications, one is forced to spoil this structure with some perturbations (e.g., by imposing boundary conditions upon a finite difference discretization of an initial-boundary value problem). How do such

perturbations affect the eigenvalues? This talk will address this question for "localized" perturbations, by which we mean perturbations that are restricted to a single entry, or a block of entries whose size remains fixed as the matrix dimension grows. One finds, for a broad class of matrices, that sufficiently small perturbations fail to alter the spectrum, though the spectrum is exponentially sensitive to other perturbations. For larger real single-entry

perturbations, one observes the perturbed eigenvalues trace out curves in the complex plane. We'll show a number of illustrations of this phenomenon for tridiagonal Toeplitz matrices.

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This talk describes collaborative work with Albrecht Boettcher, Marko Lindner, and Viatcheslav Sokolov of TU Chemnitz.

We investigate the use of interior-point and semismooth methods for solving

quadratic programming problems with a small number of linear constraints,

where the quadratic term consists of a low-rank update to a positive

semi-definite matrix. Several formulations of the support vector machine

fit into this category. An interesting feature of these particular problems

is the volume of data, which can lead to quadratic programs with between 10

and 100 million variables and, if written explicitly, a dense $Q$ matrix.

Our codes are based on OOQP, an object-oriented interior-point code, with the

linear algebra specialized for the support vector machine application.

For the targeted massive problems, all of the data is stored out of core and

we overlap computation and I/O to reduce overhead. Results are reported for

several linear support vector machine formulations demonstrating that the

methods are reliable and scalable and comparing the two approaches.

Non-selfadjoint singular differential equation eigenproblems arise in a number of contexts, including scattering theory, the study of quantum-mechanical resonances, and hydrodynamic and magnetohydrodynamic stability theory.

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It is well known that the spectra of non-selfadjoint operators can be pathologically sensitive to perturbation of the

operator. Wilkinson provides matrix examples in his classical text, while Trefethen has studied the phenomenon extensively through pseudospectra, which he argues are often of more physical relevance than the spectrum itself. E.B. Davies has studied the phenomenon particularly in the context of Sturm-Liouville operators and has shown that the eigenfunctions and associated functions of non-selfadjoint singular Sturm-Liouville operators may not even form a complete set in $L^2$.

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In this work we ask the question: under what conditions can one expect the regularization process used for selfadjoint singular Sturm-Liouville operators to be successful for non-selfadjoint operators? The answer turns out to depend in part on the so-called Sims Classification of the problem. For Sims Case I the process is not guaranteed to work, and indeed Davies has very recently described the way in which spurious eigenvalues may be generated and converge to certain curves in the complex plane.

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Using the Titchmarsh-Weyl theory we develop a very simple numerical procedure which can be used a-posteriori to distinguish genuine eigenvalues from spurious ones. Numerical results indicate that it is able to detect not only the spurious eigenvalues due to the regularization process, but also spurious eigenvalues due to the numerics on an already-regular problem. We present applications to quantum mechanical resonance calculations and to the Orr-Sommerfeld equation.

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This work, in collaboration with B.M. Brown in Cardiff, has recently been generalized to Hamiltonian systems.

Stiff systems of ODEs arise commonly when solving PDEs by spectral methods,

so conventional explicit time-stepping methods require very small time steps.

The stiffness arises predominantly through the linear terms, and these

terms can be handled implicitly or exactly, permitting larger time steps.

This work develops and investigates a class of methods known as

'exponential time differencing'. These methods are shown to have a

number of advantages over the more well-known linearly implicit

methods and integrating factor methods.

The Kestrel interface for submitting optimization problems to the NEOS Server augments the established e-mail, socket, and web interfaces by enabling easy usage of remote solvers from a local modeling environment.

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Problem generation, including the run-time detection of syntax errors, occurs on the local machine using any available modeling language facilities. Finding a solution to the problem takes place on a remote machine, with the result returned in the native modeling language format for further processing. A byproduct of the Kestrel interface is the ability to solve multiple problems generated by a modeling language in parallel.

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This mechanism is used, for example, in the GAMS/AMPL solver available through the NEOS Server, which internally translates a submitted GAMS problem into AMPL. The resulting AMPL problem is then solved through the NEOS Server via the Kestrel interface. An advantage of this design is that the GAMS to AMPL translator does not need to be collocated with the AMPL solver used, removing restrictions on solver choice and reducing administrative costs.

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This talk is joint work with Elizabeth Dolan.

Tridiagonal matrices and three term recurrences and second order equations appear amazingly often, throughout all of mathematics. We won't try to review this subject. Instead we look in two less familiar directions.

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Here is a tridiagonal matrix problem that waited surprisingly long for a solution. Forward elimination factors T into LDU, with the pivots in D as usual. Backward elimination, from row n to row 1, factors T into U_D_L_. Parlett asked for a proof that diag(D + D_) = diag(T) + diag(T^-1).^-1. In an excellent paper (Lin Alg Appl 1997) Dhillon and Parlett extended this four-diagonal identity to block tridiagonal matrices, and also applied it to their "Holy Grail" algorithm for the eigenproblem. I would like to make a different connection, to the Kalman filter.

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The second topic is a generalization of tridiagonal to "tree-diagonal". Unlike the interval, the tree can branch. In the matrix T, each vertex is connected only to its neighbors (but a branch point has more than two neighbors). The continuous analogue is a second order differential equation on a tree. The "non-jump" conditions at a meeting of N edges are continuity of the potential (N-1 equations) and Kirchhoff's Current Law (1 equation). Several important properties of tridiagonal matrices, including O(N) algorithms, survive on trees.