Functions defined by evaluation programs involving smooth  elementals and absolute values as well as max and min are piecewise smooth. For this class we present first and second order, necessary and sufficient conditions for the functions to be locally optimal, or convex, or at least possess a supporting hyperplane. The conditions generalize the classical KKT and SSC theory and are constructive; though in the case of convexity they may be combinatorial to verify. As a side product we find that, under the Mangasarin-Fromowitz-Kink-Qualification, the well established nonsmooth concept of subdifferential regularity is equivalent to first order convexity. All results are based on piecewise linearization and suggest corresponding optimization algorithms.

In variational imaging and other inverse problem modeling, regularisation plays a major role. In recent years, high order regularizers such as the total generalised variation, the mean curvature and the Gaussian curvature are increasingly studied and applied, and many improved results over the widely-used total variation model are reported.
Here we first introduce the fractional order derivatives and the total fractional-order variation which provides an alternative  regularizer and is not yet formally analysed. We demonstrate that existence and uniqueness properties of the new model can be analysed in a fractional BV space, and, equally, the new model performs as well as the high order regularizers (which do not yet have much theory). 
In the usual framework, the algorithms of a fractional order model are not fast due to dense matrices involved. Moreover, written in a Bregman framework, the resulting Sylvester equation with Toeplitz coefficients can be solved efficiently by a preconditioned solver. Further ideas based on adaptive integration can also improve the computational efficiency in a dramatic way.
 Numerical experiments will be given to illustrate the advantages of the new regulariser for both restoration and registration problems.
 

Work with Jemima Tabeart, Sarah Dance, Nancy Nichols, Joanne Waller (University of Reading) and Stefano Migliorini, Fiona Smith (Met Office). 
In environmental prediction variational data assimilation (DA) is a method for using observational data to estimate the current state of the system. The DA problem is usually solved as a very large nonlinear least squares problem, in which the fit to the measurements is balanced against the fit to a previous model forecast. These two terms are weighted by matrices describing the correlations of the errors in the forecast and in the observations. Until recently most operational weather and ocean forecasting systems assumed that the errors in the observations are uncorrelated. However, as we move to higher resolution observations then it is becoming more important to specify observation error correlations. In this work we look at the effect this has on the conditioning of the optimization problem. In the context of a linear system we develop bounds on the condition number of the problem in the presence of correlated observation errors. We show that the condition number is very dependent on the minimum eigenvalue of the observation error correlation matrix. We then present results using the Met Office data assimilation system, in which different methods for reconditioning the correlation matrix are tested. We investigate the effect of these different methods on the conditioning and the final solution of the problem.
 

In 1923, Dulac published a proof of the claim that every real analytic vector field on the plane has only finitely many limit cycles (now known as Dulac's Problem). In the mid-1990s, Ilyashenko completed Dulac's proof; his completion rests on the construction of a quasianalytic class of functions. Unfortunately, this class has very few known closure properties. For various reasons I will explain, we are interested in constructing a larger quasianalytic class that is also a Hardy field. This can be achieved using Ilyashenko's idea of superexact asymptotic expansion.  (Joint work with Tobias Kaiser)

This  concerns recent work with P. Corvaja in which we relate the Hilbert Property for an algebraic variety (a kind of axiom linked with Hilbert Irreducibility, relevant e.g. for the Inverse Galois Problem)  with the fundamental group of the variety.
 In particular, this leads to new examples (of surfaces) of  failure of the Hilbert Property. We also prove the Hilbert Property for a non-rational surface (whereas all previous examples involved rational varieties).

Motivated by the Dixmier-Moeglin equivalence, which belongs to the realm of algebra representations, we look at a differential version of this equivalence for algebraic D-groups, which belong to the realm of finite Morley rank groups in differentially closed fields. We will see how the proof of this equivalence reduces to a standard model-theoretic fact (on binding groups). Time permitting we will present an application to Hopf-Ore extensions. This is joint work with J. Bell and R. Moosa.

I will explain the model theoretic notion of ampleness
and present the geometric context of recent constructions.