Past

We consider an investment model that can operate in two different

modes. The transition from one mode to the other one is immediate and forms a

sequence of costly decisions made by the investment's management. Each of the

two modes is associated with a rate of payoff that is a function of a state

process which can be an economic indicator such as the price of a given

comodity. We model the state process by a general one-dimensional

diffusion. The objective of the problem is to determine the switching

strategy that maximises a long-term average criterion in a pathwise

sense. Our analysis results in analytic solutions that can easily be

computed, and exhibit qualitatively different optimal behaviours.

The process industries are one of the UK's major sectors and include

petrochemicals, pharmaceuticals, water, energy and the food industry,

amongst others. The design of a processing plant is a difficult task. This

is due to both the need to cater for multiple criteria (such as economics,

environmental and safety) and the use highly complex nonlinear models to

describe the behaviour of individual unit operations in the process. Early

in the design stages, an engineer may wish to use automated design tools to

generate conceptual plant designs which have potentially positive attributes

with respect to the main criteria. Such automated tools typically rely on

optimization for solving large mixed integer nonlinear programming models.

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This talk presents an overview of some of the work done in the Computer

Aided Process Engineering group at UCL. Primary emphasis will be given to

recent developments in hybrid optimization methods, including the use of

graphical interfaces based on problem specific visualization techniques to

allow the engineer to interact with embedded optimization procedures. Case

studies from petrochemical and water industries will be presented to

demonstrate the complexities involved and illustrate the potential benefits

of hybrid approaches.

http://www.maths.ox.ac.uk/notices/events/abstracts/stochastic-analysis/…

The simulation of sedimentary basins aims at reconstructing its historical

evolution in order to provide quantitative predictions about phenomena

leading to hydrocarbon accumulations. The kernel of this simulation is the

numerical solution of a complex system of time dependent, three

dimensional partial differential equations of mixed parabolic-hyperbolic

type in highly heterogeneous media. A discretisation and linearisation of

this system leads to large ill-conditioned non-symmetric linear systems

with three unknowns per mesh element.

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In the seminar I will look at different preconditioning approaches for

these systems and at their parallelisation. The most effective

preconditioner which we developed so far consists in three stages: (i) a

local decoupling of the equations which (in addition) aims at

concentrating the elliptic part of the system in the "pressure block'';

(ii) an efficient preconditioning of the pressure block using AMG; (iii)

the "recoupling'' of the equations. Numerical results on real case

studies, exhibit (i) a significant reduction of sequential CPU times, up

to a factor 5 with respect to the current ILU(0) preconditioner, (ii)

robustness with respect to physical and numerical parameters, and (iii) a

speedup of up to 4 on 8 processors.

Why does a spinning coin come to such a sudden stop? Why does a
spinning hard-boiled egg stand up on its end? And why does the
rattleback rotate happily in one direction but not in the other?
The key mathematical aspects of these familiar dynamical phenomena,
which admit simple table-top demonstration, will be revealed.

Rapidly oscillating problems, whether differential equations or

integrals, ubiquitous in applications, are allegedly difficult to

compute. In this talk we will endeavour to persuade the audience that

this is false: high oscillation, properly understood, is good for

computation! We describe methods for differential equations, based on

Magnus and Neumann expansions of modified systems, whose efficacy

improves in the presence of high oscillation. Likewise, we analyse

generalised Filon quadrature methods, showing that also their error

sharply decreases as the oscillation becomes more rapid.