Forthcoming events in this series


Tue, 08 May 2018

14:00 - 15:00
L5

Discontinuous Galerkin method for the Oseen problem with mixed boundary conditions: a priori and aposteriori error analyses

Nour Seloula
(Caen)
Abstract

We introduce and analyze a discontinuous Galerkin method for the Oseen equations in two dimension spaces. The boundary conditions are mixed and they are assumed to be of three different types:
the vorticity  and the normal component of the velocity are given on a first part of the boundary, the pressure and the tangential component of the velocity are given on a second part of the boundary and the Dirichlet condition is given on the remainder part . We establish a priori error estimates in the energy norm for the velocity and in the L2 norm for the pressure. An a posteriori error estimate is also carried out yielding optimal convergence rate. The analysis is based on rewriting the method in a non-consistent manner using lifting operators in the spirit of Arnold, Brezzi, Cockburn and Marini.

Tue, 01 May 2018

14:30 - 15:00
L5

Weakly-normal basis vector fields in RKHS with an application to shape Newton methods

Alberto Paganini
(Oxford)
Abstract

We construct a space of vector fields that are normal to differentiable curves in the plane. Its basis functions are defined via saddle point variational problems in reproducing kernel Hilbert spaces (RKHSs). First, we study the properties of these basis vector fields and show how to approximate them. Then, we employ this basis to discretise shape Newton methods and investigate the impact of this discretisation on convergence rates.

Tue, 01 May 2018

14:00 - 14:30
L5

Scalable Least-Squares Minimisation for Bundle Adjustment Problem

Lindon Roberts
(Oxford)
Abstract

Structure from Motion (SfM) is a problem which asks: given photos of an object from different angles, can we reconstruct the object in 3D? This problem is important in computer vision, with applications including urban planning and autonomous navigation. A key part of SfM is bundle adjustment, where initial estimates of 3D points and camera locations are refined to match the images. This results in a high-dimensional nonlinear least-squares problem, which is typically solved using the Gauss-Newton method. In this talk, I will discuss how dimensionality reduction methods such as block coordinates and randomised sketching can be used to improve the scalability of Gauss-Newton for bundle adjustment problems.

Tue, 24 Apr 2018

14:30 - 15:00
L3

Randomized algorithms for computing full, rank-revealing factorizations

Abinand Gopal
(Oxford)
Abstract

Over the past decade, the randomized singular value decomposition (RSVD) algorithm has proven to be an efficient, reliable alternative to classical algorithms for computing low-rank approximations in a number of applications. However, in cases where no information is available on the singular value decay of the data matrix or the data matrix is known to be close to full-rank, the RSVD is ineffective. In recent years, there has been great interest in randomized algorithms for computing full factorizations that excel in this regime.  In this talk, we will give a brief overview of some key ideas in randomized numerical linear algebra and introduce a new randomized algorithm for computing a full, rank-revealing URV factorization.

Tue, 24 Apr 2018

14:00 - 14:30
L3

Block preconditioners for non-isothermal flow through porous media

Thomas Roy
(Oxford)
Abstract

In oil and gas reservoir simulation, standard preconditioners involve solving a restricted pressure system with AMG. Initially designed for isothermal models, this approach is often used in the thermal case. However, it does not incorporate heat diffusion or the effects of temperature changes on fluid flow through viscosity and density. We seek to develop preconditioners which consider this cross-coupling between pressure and temperature. In order to study the effects of both pressure and temperature on fluid and heat flow, we first consider a model of non-isothermal single phase flow through porous media. By focusing on single phase flow, we are able to isolate the properties of the pressure-temperature subsystem. We present a numerical comparison of different preconditioning approaches including block preconditioners.

Thu, 22 Mar 2018

14:00 - 15:00
C1

The Usefulness of a Modified Restricted Isometry Property

Simon Foucart
(Texas A&M University)
Abstract

The restricted isometry property is arguably the most prominent tool in the theory of compressive sensing. In its classical version, it features l_2 norms as inner and outer norms. The modified version considered in this talk features the l_1 norm as the inner norm, while the outer norm depends a priori on the distribution of the random entries populating the measurement matrix.  The modified version holds for a wider class of random matrices and still accounts for the success of sparse recovery via basis pursuit and via iterative hard thresholding. In the special case of Gaussian matrices, the outer norm actually reduces to an l_2 norm. This fact allows one to retrieve results from the theory of one-bit compressive sensing in a very simple way. Extensions to one-bit matrix recovery are then straightforward.
 

Tue, 06 Mar 2018

14:30 - 15:00
L5

Predicting diagnosis and cognitive measures for Alzheimer’s disease

Paul Moore
(Oxford University)
Abstract

Forecasting a diagnosis of Alzheimer’s disease is a promising means of selection for clinical trials of Alzheimer’s disease therapies. A positive PET scan is commonly used as part of the inclusion criteria for clinical trials, but PET imaging is expensive, so when a positive scan is one of the trial inclusion criteria it is desirable to avoid screening failures. In this talk I will describe a scheme for pre-selecting participants using statistical learning methods, and investigate how brain regions change as the disease progresses.  As a means of generating features I apply the Chen path signature. This is a systematic way of providing feature sets for multimodal data that can probe the nonlinear interactions in the data as an extension of the usual linear features. While it can easily perform a traditional analysis, it can also probe second and higher order events for their predictive value. Combined with Lasso regularisation one can auto detect situations where the observed data has nonlinear information.

Tue, 06 Mar 2018

14:00 - 14:30
L5

Achieving high performance through effective vectorisation

Oliver Sheridan-Methven
(InFoMM)
Abstract

The latest CPUs by Intel and ARM support vectorised operations, where a single set of instructions (e.g. add, multiple, bit shift, XOR, etc.) are performed in parallel for small batches of data. This can provide great performance improvements if each parallel instruction performs the same operation, but carries the risk of performance loss if each needs to perform different tasks (e.g. if else conditions). I will present the work I have done so far looking into how to recover the full performance of the hardware, and some of the challenges faced when trading off between ever larger parallel tasks, risks of tasks diverging, and how certain coding styles might be modified for memory bandwidth limited applications. Examples will be taken from finance and Monte Carlo applications, inspecting some standard maths library functions and possibly random number generation.

Tue, 27 Feb 2018

14:30 - 15:00
L5

Low-rank plus Sparse matrix recovery and matrix rigidity

Simon Vary
(Oxford University)
Abstract

Low-rank plus sparse matrices arise in many data-oriented applications, most notably in a foreground-background separation from a moving camera. It is known that low-rank matrix recovery from a few entries (low-rank matrix completion) requires low coherence (Candes et al 2009) as in the extreme cases when the low-rank matrix is also sparse, where matrix completion can miss information and be unrecoverable. However, the requirement of low coherence does not suffice in the low-rank plus sparse model, as the set of low-rank plus sparse matrices is not closed. We will discuss the relation of non-closedness of the low-rank plus sparse model to the notion of matrix rigidity function in complexity theory.

Tue, 27 Feb 2018

14:00 - 14:30
L5

Finite element approximation of the flow of incompressible fluids with implicit constitutive law

Tabea Tscherpel
(PDE-CDT)
Abstract

The object of this talk is a class of generalised Newtonian fluids with implicit constitutive law.
Both in the steady and the unsteady case, existence of weak solutions was proven by Bul\'\i{}\v{c}ek et al. (2009, 2012) and the main challenge is the small growth exponent qq and the implicit law.
I will discuss the application of a splitting and regularising strategy to show convergence of FEM approximations to weak solutions of the flow. 
In the steady case this allows to cover the full range of growth exponents and thus generalises existing work of Diening et al. (2013). If time permits, I will also address the unsteady case.
This is joint work with Endre Suli.

Tue, 20 Feb 2018

14:30 - 15:00
L5

Sparse non-negative super-resolution - simplified and stabilised

Bogdan Toader
(InFoMM)
Abstract

We consider the problem of localising non-negative point sources, namely finding their locations and amplitudes from noisy samples which consist of the convolution of the input signal with a known kernel (e.g. Gaussian). In contrast to the existing literature, which focuses on TV-norm minimisation, we analyse the feasibility problem. In the presence of noise, we show that the localised error is proportional to the level of noise and depends on the distance between each source and the closest samples. This is achieved using duality and considering the spectrum of the associated sampling matrix.

Tue, 20 Feb 2018

14:00 - 14:30
L5

Inverse Problems in Electrochemistry

Katherine Gillow
(Oxford University)
Abstract

A simple experiment in the field of electrochemistry involves  controlling the applied potential in an electrochemical cell. This  causes electron transfer to take place at the electrode surface and in turn this causes a current to flow. The current depends on parameters in  the system and the inverse problem requires us to estimate these  parameters given an experimental trace of the current. We briefly  describe recent work in this area from simple least squares approximation of the parameters, through bootstrapping to estimate the distributions of the parameters, to MCMC methods which allow us to see correlations between parameters.

Tue, 13 Feb 2018

14:30 - 15:00
L5

From Convolutional Sparse Coding to Deep Sparsity and Neural Networks

Jeremias Sulam
(Technion Israel)
Abstract

Within the wide field of sparse approximation, convolutional sparse coding (CSC) has gained considerable attention in the computer vision and machine learning communities. While several works have been devoted to the practical aspects of this model, a systematic theoretical understanding of CSC seems to have been left aside. In this talk, I will present a novel analysis of the CSC problem based on the observation that, while being global, this model can be characterized and analyzed locally. By imposing only local sparsity conditions, we show that uniqueness of solutions, stability to noise contamination and success of pursuit algorithms are globally guaranteed. I will then present a Multi-Layer extension of this model and show its close relation to Convolutional Neural Networks (CNNs). This connection brings a fresh view to CNNs, as one can attribute to this architecture theoretical claims under local sparse assumptions, which shed light on ways of improving the design and implementation of these networks. Last, but not least, we will derive a learning algorithm for this model and demonstrate its applicability in unsupervised settings.

Tue, 13 Feb 2018

14:00 - 14:30
L5

Cubic Regularization Method Revisited: Quadratic Convergence to Degenerate Solutions and Applications to Phase Retrieval and Low-rank Matrix Recovery

Man-Chung Yue
(Imperial College)
Abstract

In this talk, we revisit the cubic regularization (CR) method for solving smooth non-convex optimization problems and study its local convergence behaviour. In their seminal paper, Nesterov and Polyak showed that the sequence of iterates of the CR method converges quadratically a local minimum under a non-degeneracy assumption, which implies that the local minimum is isolated. However, many optimization problems from applications such as phase retrieval and low-rank matrix recovery have non-isolated local minima. In the absence of the non-degeneracy assumption, the result was downgraded to the superlinear convergence of function values. In particular, they showed that the sequence of function values enjoys a superlinear convergence of order 4/3 (resp. 3/2) if the function is gradient dominated (resp. star-convex and globally non-degenerate). To remedy the situation, we propose a unified local error bound (EB) condition and show that the sequence of iterates of the CR method converges quadratically a local minimum under the EB condition. Furthermore, we prove that the EB condition holds if the function is gradient dominated or if it is star-convex and globally non-degenerate, thus improving the results of Nesterov and Polyak in three aspects: weaker assumption, faster rate and iterate instead of function value convergence. Finally, we apply our results to two concrete non-convex optimization problems that arise from phase retrieval and low-rank matrix recovery. For both problems, we prove that with overwhelming probability, the local EB condition is satisfied and the CR method converges quadratically to a global optimizer. We also present some numerical results on these two problems.

Tue, 06 Feb 2018

14:30 - 15:00
L5

The number of distinct eigenvalues of a matrix after perturbation

Patrick Farrell
(Oxford University)
Abstract


The question of what happens to the eigenvalues of a matrix after an additive perturbation has a long history, with notable contributions from Wilkinson, Sorensen, Golub, H\"ormander, Ipsen and Mehrmann, among many others. If the perturbed matrix $C \in \mathbb{C}^{n \times n}$ is given by $C = A + B$, these theorems typically consider the case where $A$ and/or $B$ are symmetric and $B$ has rank one. In this talk we will prove a theorem that bounds the number of distinct eigenvalues of $C$ in terms of the number of distinct eigenvalues of $A$, the diagonalisability of $A$, and the rank of $B$. This new theorem is more general in that it applies to arbitrary matrices $A$ perturbed by matrices of arbitrary rank $B$. We will also discuss various refinements of my bound recently developed by other authors.
 

Tue, 06 Feb 2018

14:00 - 14:30
L5

Finite element approximation of chemically reacting non-Newtonian fluids

Seungchan Ko
(OxPDE)
Abstract

We consider a system of nonlinear partial differential equations modelling the steady motion of an incompressible non-Newtonian fluid, which is chemically reacting. The governing system consists of a steady convection-diffusion equation for the concentration and the generalized steady Navier–Stokes equations, where the viscosity coefficient is a power-law type function of the shear-rate, and the coupling between the equations results from the concentration-dependence of the power-law index. This system of nonlinear partial differential equations arises in mathematical models of the synovial fluid found in the cavities of moving joints. We construct a finite element approximation of the model and perform the mathematical analysis of the numerical method. Key technical tools include discrete counterparts of the Bogovski operator, De Giorgi’s regularity theorem and the Acerbi–Fusco Lipschitz truncation of Sobolev functions, in function spaces with variable integrability exponents.

Tue, 30 Jan 2018

14:30 - 15:00
L5

Study of Newton Method on singularity of Vector Fields

Jinyun Yuan
(Brazil)
Abstract

In this talk we discuss the convergence rate of the Newton method for finding the singularity point on vetor fields. It is well-known that the Newton Method has local quadratic convergence rate with nonsingularity and Lipschitz condition. Here we release Lipschitz condition. With only nonsingularity, the Newton Method has superlinear convergence. If we have enough time, we can quickly give the damped Newton method on finding singularity on vector fields with superlinear convergence under nonsingularity condition only.

Tue, 30 Jan 2018

14:00 - 14:30
L5

Mass loss in fragmentation models

Graham Baird
(OxPDE)
Abstract

In this talk we consider the issue of mass loss in fragmentation models due to 'shattering'. As a solution we propose a hybrid discrete/continuous model whereby the smaller particles are considered as having discrete mass, whilst above a certain cut-off, mass is taken to be a continuous variable. The talk covers the development of such a model, its initial analysis via the theory of operator semigroups and its numerical approximation using a finite volume discretisation.

Tue, 23 Jan 2018

14:30 - 15:00
L5

Multipreconditioning for two-phase flow

Niall Bootland
(InFoMM)
Abstract

We explore the use of applying multiple preconditioners for solving linear systems arising in simulations of incompressible two-phase flow. In particular, we use a selective MPGMRES algorithm, for which the search space grows linearly throughout the iterative solver, and block preconditioners based on Schur complement approximations

Tue, 23 Jan 2018

14:00 - 14:30
L5

A discontinuous Galerkin finite element method for Hamilton–Jacobi–Bellman equations on piecewise curved domains, with applications to Monge–Ampère type equations

Ellya Kawecki
(OxPDE)
Abstract

We introduce a discontinuous Galerkin finite element method (DGFEM) for Hamilton–Jacobi–Bellman equations on piecewise curved domains, and prove that the method is consistent, stable, and produces optimal convergence rates. Upon utilising a long standing result due to N. Krylov, we may characterise the Monge–Ampère equation as a HJB equation; in two dimensions, this HJB equation can be characterised further as uniformly elliptic HJB equation, allowing for the application of the DGFEM

Tue, 16 Jan 2018

14:30 - 15:00
L5

Parameter estimation with forward operators

Ozzy Nilsen
(InFoMM)
Abstract

We propose a new parameter estimation technique for SDEs, based on the inverse problem of finding a forward operator describing the evolution of temporal data. Nonlinear dynamical systems on a state-space can be lifted to linear dynamical systems on spaces of higher, often infinite, dimension. Recently, much work has gone into approximating these higher-dimensional systems with linear operators calculated from data, using what is called Dynamic Mode Decomposition (DMD). For SDEs, this linear system is given by a second-order differential operator, which we can quickly calculate and compare to the DMD operator.

Tue, 16 Jan 2018

14:00 - 14:30
L5

Numerically Constructing Measure-Valued Solutions

Miles Caddick
(OxPDE)
Abstract

In 2016-17, Fjordholm, Kappeli, Mishra and Tadmor developed a numerical method by which one could compute measure-valued solutions to systems of hyperbolic conservation laws with either measure-valued or deterministic initial data. In this talk I will discuss the ideas behind this method, and discuss how it can be adapted to systems of quasi-linear parabolic PDEs whose nonlinearity fails to satisfy a monotonicity condition.

Tue, 28 Nov 2017

14:30 - 15:00
L3

Shape optimization under overhang constraints imposed by additive manufacturing technologies

Charles Dapogny
(Laboratoire Jean Kuntzmann)
Abstract

The purpose of this work is to introduce a new constraint functional for shape optimization problems, which enforces the constructibility by means of additive manufacturing processes, and helps in preventing the appearance of overhang features - large regions hanging over void which are notoriously difficult to assemble using such technologies. The proposed constraint relies on a simplified model for the construction process: it involves a continuum of shapes, namely the intermediate shapes corresponding to the stages of the construction process where the total, final shape is erected only up to a certain level. The shape differentiability of this constraint functional is analyzed - which is not a standard issue because of its peculiar structure. Several numerical strategies and examples are then presented. This is a joint work with G. Allaire, R. Estevez, A. Faure and G. Michailidis.

Tue, 28 Nov 2017

14:00 - 14:30
L3

Tomosynthesis with nonlinear compressed sensing

Raphael Hauser
(University of Oxford)
Abstract

A new generation of low cost 3D tomography systems is based on multiple emitters and sensors that partially convolve measurements. A successful approach to deconvolve the measurements is to use nonlinear compressed sensing models. We discuss such models, as well as algorithms for their solution. 

Tue, 21 Nov 2017

14:30 - 15:00
L5

The Cascading Haar Wavelet algorithm for computing the Walsh-Hadamard Transform

Andrew Thompson
(University of Oxford)
Abstract

I will describe a novel algorithm for computing the Walsh Hadamard Transform (WHT) which consists entirely of Haar wavelet transforms. The algorithm shares precisely the same serial complexity as the popular divide-and-conquer algorithm for the WHT. There is also a natural way to parallelize the algorithm which appears to have a number of attractive features.