L2

Speaker: Thomas Oliver

Title: Hyperbolic circles and non-trivial zeros

Abstract: L-functions can often be considered as generating series of arithmetic information. Their non-trivial zeros are the subject of many famous conjectures, which offer countless applications to number theory. Using simple geometric observations in the hyperbolic plane, we will study the relationship between the zeros of L-functions and their characterisation amongst more general Dirichlet series.
 

Speaker: Ebrahim Patel

Title: From trains to brains: Adventures in Tropical Mathematics.

Abstract: Tropical mathematics uses the max and plus operator to linearise discrete nonlinear systems; I will present its popular application to solve scheduling problems such as railway timetabling. Adding the min operator generalises the system to allow the modelling of processes on networks. Thus, I propose applications such as disease and rumour spreading as well as neuron firing behaviour.

Elena Gal
Categorification, Quantum groups and TQFTs

Quantum groups are mathematical objects that encode (via their "category of representations”) certain symmetries which have been found in the last several dozens of years to be connected to several areas of mathematics and physics. One famous application uses representation theory of quantum groups to construct invariants of 3-dimensional manifolds. To extend this theory to higher dimensions we need to “categorify" quantum groups - in essence to find a richer structure of symmetries. I will explain how one can approach such problem.

Carolina Urzua-Torres
Why you should not do boundary element methods, so I can have all the fun.

Boundary integral equations offer an attractive alternative to solve a wide range of physical phenomena, like scattering problems in unbounded domains. In this talk I will give a simple introduction to boundary integral equations arising from PDEs, and their discretization via Galerkin BEM. I will discuss some nice mathematical features of BEM, together with their computational pros and cons. I will illustrate these points with some applications and recent research developments.
 

Since Weierstrass it has been known that there are functions that are continuous but nowhere differentiable.  A beautiful example (with probability 1) is any Brownian path.  Brownian paths can be constructed either in space, via Brownian bridge, or in Fourier space, via random Fourier series.

What about functions, which we call "smoothies", that are $C^\infty$ but nowhere analytic?  This case is less familiar but analogous, and again one can do the construction either in space or Fourier space.  We present the ideas and illustrate them with the new Chebfun $\tt{smoothie}$ command.  In the complex plane, the same idea gives functions analytic in the open unit disk and $C^\infty$ on the unit circle, which is a natural boundary.

Reconstruction of 3D images from a set of 2D X-ray projections is a standard inverse problem, particularly in medical imaging. Improvements in imaging technologies have enabled the development of a flat-panel X-ray source, comprised of an array of low-power emitters that are fired in quick succession. During a complete firing sequence, there may be shifts in the patient’s resting position which ultimately create artifacts in the final reconstruction. We present a method for correcting images with respect to unknown body motion, focusing on the case of simple rigid body motion. Image reconstructions are obtained by solving a sparse linear inverse problem, with respect to not only the underlying body but also the unknown velocity. Results find that reconstructions of a moving body can be much better than those obtained by measuring a stationary body, as long as the underlying motion is well approximated.

The standard rounding procedure in floating-point computations is round to nearest (RN). In this talk we consider an alternative rounding strategy called stochastic rounding (SR) which has the appealing property of being exact (actually exact!) in expectation. In the first part of the talk we discuss recent developments in probabilistic rounding error analysis and we show how rounding errors grow at an O(\sqrt{n}) rate rather than O(n) when SR is employed. This shows that Wilkinson's rule of thumb provably holds for this type of rounding. In the second part of the talk we consider the application of SR to parabolic PDEs admitting a steady state solution. We show that when the heat equation is solved in half precision RN fails to compute an accurate solution, while SR successfully solves the problem to decent accuracy.
 

We consider the problem of dynamically pricing multiple products on a network of resources, such as that faced by an airline selling tickets on its route network. For computational reasons this inherently stochastic problem is often approximated deterministically, however even the deterministic dynamic pricing problem can be impractical to solve. For this reason we have derived a novel iterative Quadratic Programming approximation to the deterministic dynamic pricing problem that is not only very fast to solve in practice but also exhibits a provably linear rate of convergence. This is joint work with Saksham Jain and Raphael Hauser.
 

Randomized SVD has become an extremely successful approach for efficiently computing a low-rank approximation of matrices. In particular the paper by Halko, Martinsson (who is speaking twice this week), and Tropp (SIREV 2011) contains extensive analysis, and made it a very popular method. 
The complexity for $m\times n$ matrices is $O(Nr+(m+n)r^2)$ where $N$ is the cost of a (fast) matrix-vector multiplication; which becomes $O(mn\log n+(m+n)r^2)$ for dense matrices. This work uses classical results in numerical linear algebra to reduce the computational cost to $O(Nr)$ without sacrificing numerical stability. The cost is essentially optimal for many classes of matrices, including $O(mn\log n)$ for dense matrices. The method can also be adapted for updating, downdating and perturbing the matrix, and is especially efficient relative to previous algorithms for such purposes.  

Robust principal component analysis and low-rank matrix completion are extensions of PCA that allow for outliers and missing entries, respectively. Solving these problems requires a low coherence between the low-rank matrix and the canonical basis. However, in both problems the well-posedness issue is even more fundamental; in some cases, both Robust PCA and matrix completion can fail to have any solutions due to the fact that the set of low-rank plus sparse matrices is not closed. Another consequence of this fact is that the lower restricted isometry property (RIP) bound cannot be satisfied for some low-rank plus sparse matrices unless further restrictions are imposed on the constituents. By restricting the energy of one of the components, we close the set and are able to derive the RIP over the set of low rank plus sparse matrices and operators satisfying concentration of measure inequalities. We show that the RIP of an operator implies exact recovery of a low-rank plus sparse matrix is possible with computationally tractable algorithms such as convex relaxations or line-search methods. We propose two efficient iterative methods called Normalized Iterative Hard Thresholding (NIHT) and Normalized Alternative Hard Thresholding (NAHT) that provably recover a low-rank plus sparse matrix from subsampled measurements taken by an operator satisfying the RIP.