University of Southampton

When high-dimensional problems are concerned, not much algorithms can break the curse of dimensionality, and solve them efficiently and reliably. Among those, tensor product algorithms, which implement the idea of separation of variables for multi-index arrays (tensors), seem to be the most general and also very promising. They originated in quantum physics and chemistry and descent broadly from the density matrix renormalization group (DMRG) and matrix product states (MPS) formalisms. The same tensor formats were recently re-discovered in the numerical linear algebra (NLA) community as the tensor train (TT) format.

Algorithms developed in the quantum physics community are based on the optimisation in tensor formats, that is performed subsequently for all components of a tensor format (i.e. all sites or modes).
The DMRG/MPS schemes are very efficient but very difficult to analyse, and at the moment only local convergence results for the simplest algorithm are available. In the NLA community, a common approach is to use a classical iterative scheme (e.g. GMRES) and enforce the compression to a tensor format at every step. The formal analysis is quite straightforward, but tensor ranks of the vectors which span the Krylov subspace grow rapidly with iterations, and the methods are struggling in practice.

The first attempt to merge classical iterative algorithms and DMRG/MPS methods was made by White (2005), where the second Krylov vector is used to expand the search space on the optimisation step.
The idea proved to be useful, but the implementation was based on the fair amount of physical intuition, and the algorithm is not completely justified.

We have recently proposed the AMEn algorithm for linear systems, that also injects the gradient direction in the optimisation step, but in a way that allows to prove the global convergence of the resulted scheme. The scheme can be easily applied for the computation of the ground state --- the differences to the algorithm of S. White are emphasized in Dolgov and Savostyanov (2013).
The AMEn scheme is already acknowledged in the NLA community --- for example it was recently applied for the computation of extreme eigenstates by Kressner, Steinlechner and Uschmajew (2013), using the block-TT format proposed by in Dolgov, Khoromskij, Oseledets and Savostyanov (2014).

At the moment, AMEn algorithm was applied
 - to simulate the NMR spectra of large molecules (such as ubiquitin),
 - to solve the Fokker-Planck equation for the non-Newtonian polymeric flows,
 - to the chemical master equation describing the mesoscopic model of gene regulative networks,
 - to solve the Heisenberg model problem for a periodic spin chain.
We aim to extend this framework and the analysis to other problems of NLA: eigenproblems, time-dependent problems, high-dimensional interpolation, and matrix functions;  as well as to a wider list of high-dimensional problems.

This is a joint work with Sergey Dolgov the from Max-Planck Institute for Mathematics in the Sciences, Leipzig, Germany.

We propose a new algorithm for the approximate solution of large-scale high-dimensional tensor-structured linear systems. It can be applied to high-dimensional differential equations, which allow a low-parametric approximation of the multilevel matrix, right-hand side and solution in a tensor product format. We apply standard one-site tensor optimisation algorithm (ALS), but expand the tensor manifolds using the classical iterative schemes (e.g. steepest descent).  We obtain the rank--adaptive algorithm with the theoretical convergence estimate not worse than the one of the steepest descent, and fast practical convergence, comparable or even better than the convergence of more expensive two-site optimisation algorithm (DMRG).
The method is successfully applied for a high--dimensional problem of quantum chemistry, namely the NMR simulation of a large peptide.

This is a joint work with S.Dolgov (Max-Planck Institute, Leipzig, Germany), supported by RFBR and EPSRC grants.

Keywords: high--dimensional problems, tensor train format, ALS, DMRG, steepest descent, convergence rate, superfast algorithms, NMR.

Group actions play an important role in both topological problems and coarse geometric conjectures. I will introduce the idea of a partial action of a group on a metric space and explain, in the case of certain classes of coarsely disconnected spaces, how partial actions can be used to give a geometric proof of a result of Willett and Yu concerning the coarse Baum-Connes conjecture.

 Graph products of groups naturally generalize direct and free products and have a rich subgroup structure. Basic examples of graph products are right angled Coxeter and Artin groups. I will discuss various forms of Tits Alternative for subgroups and
their stability under graph products. The talk will be based on a joint work with Yago Antolin Pichel.

This work forms part of a larger research project to develop efficient

low-dissipative high-order numerical techniques for high-speed

turbulent flow simulation, including shock wave interactions with

turbulence. The requirements on a numerical method are stringent.For

the turbulence the method must be capable of resolving accurately a

wide range of length scales, whilst for shock waves the method must be

stable and not generate excessive local oscillations. Conventional

methods are either too dissipative, or incapable of shock capturing.

Higher-order ENO, WENO or hybrid schemes are too expensive for

practical computations. Previous work of Yee, Sandham & Djomehri

(1999) developed high-order shock-capturing schemes which minimize the

use of numerical dissipation away from shock

waves. The objective of the present study is to further minimize the

use of numerical dissipation for shock-free compressible turbulence

simulations.

The satisfiability (SAT) problem is a central problem in mathematical

logic, computing theory, and artificial intelligence. We consider

instances of SAT specified by a set of boolean variables and a

propositional formula in conjunctive normal form. Given such an instance,

the SAT problem asks whether there is a truth assignment to the variables

such that the formula is satisfied. It is well known that SAT is in

general NP-complete, although several important special cases can be

solved in polynomial time. Extending the work of de Klerk, Warners and van

Maaren, we present new linearly sized semidefinite programming (SDP)

relaxations arising from a recently introduced paradigm of higher

semidefinite liftings for discrete optimization problems. These

relaxations yield truth assignments satisfying the SAT instance if a

feasible matrix of sufficiently low rank is computed. The sufficient rank

values differ between relaxations and can be viewed as a measure of the

relative strength of each relaxation. The SDP relaxations also have the

ability to prove that a given SAT formula is unsatisfiable. Computational

results on hard instances from the SAT Competition 2003 show that the SDP

approach has the potential to complement existing techniques for SAT.

The talk starts with a general introduction of the convex

quadratic semidefinite programming problem (QSDP), followed by a number of

interesting examples arising from finance, statistics and computer sciences.

We then discuss the concept of primal nondegeneracy for QSDP and show that

some QSDPs are nondegenerate and others are not even under the linear

independence assumption. The talk then moves on to the algorithmic side by

introducing the dual approach and how it naturally leads to Newton's method,

which is quadratically convergent for degenerate problems. On the

implementation side of the Newton method, we stress that direct methods for

the linear equations in Newton's method are impossible simply because the

equations are quite large in size and dense. Our numerical experiments use

the conjugate gradient method, which works quite well for the nearest

correlation matrix problem. We also discuss difficulties for us to find

appropriate preconditioners for the linear system encountered. The talk

concludes in discussing some other available methods and some future topics.

The significance of the effects of non-healing wounds has been the topic of many research papers and lectures during the last 25 years. Efforts have been made to understand the effects of long-standing venous hypertension, diabetes, the prevalence of wounds in such conditions with as well as the difficulties faced in managing such wounds with some success. Successful efforts to define standard care regimes have also been made. However, attempts to introduce innovative therapy have been much less successful. Is this merely because we have not understood the intricacies of the problem? And would system based modelling be an untried technique?

Venous ulcers are the majority of lower extremity wounds, and a clinical challenge. A previously developed model of venous ulcers permits some understanding of why compression bandaging is successful but fails to accommodate complications such as exudate and infection. Could this experimental model be improved by system based modelling?

Chronic wounds need to be modelled however the needs for such models should be examined in order that the outcome permits advances in our thinking as well in clinical management.