New perspectives for higher-order methods in convex optimisation
This colloquium is the annual Maths-Stats colloquium, held jointly with the Statistics department.
This colloquium is the annual Maths-Stats colloquium, held jointly with the Statistics department.
This seminar will be held via zoom. Meeting link will be sent to members of our mailing list (https://lists.maths.ox.ac.uk/mailman/listinfo/random-matrix-theory-anno…) in our weekly announcement on Monday.
Averages of multiplicative eigenvalue statistics of Haar distributed unitary matrices are Toeplitz determinants, and asymptotics for these determinants are now well understood for large classes of symbols, including symbols with gaps and (merging) Fisher-Hartwig singularities. Similar averages for Haar distributed orthogonal matrices are Toeplitz+Hankel determinants. Some asymptotic results for these determinants are known, but not in the same generality as for Toeplitz determinants. I will explain how one can systematically deduce asymptotics for averages in the orthogonal group from those in the unitary group, using a transformation formula and asymptotics for certain orthogonal polynomials on the unit circle, and I will show that this procedure leads to asymptotic results for symbols with gaps or (merging) Fisher-Hartwig singularities. The talk will be based on joint work with Gabriel Glesner, Alexander Minakov and Meng Yang.
Ginzburg–Landau type functionals provide a relaxation scheme to construct harmonic maps in the presence of topological obstructions. They arise in superconductivity models, in liquid crystal models (Landau–de Gennes functional) and in the generation of cross-fields in meshing. For a general compact manifold target space we describe the asymptotic number, type and location of singularities that arise in minimizers. We cover in particular the case where the fundamental group of the vacuum manifold in nonabelian and hence the singularities cannot be characterized univocally as elements of the fundamental group. The results unify the existing theory and cover new situations and problems.
This is a joint work with Antonin Monteil and Rémy Rodiac (UCLouvain, Louvain- la-Neuve, Belgium)
STAR-Vote is voting system that results from a collaboration between a number of
academics and the Travis County, Texas elections office, which currently uses a
DRE voting system and previously used an optical scan voting system. STAR-Vote
represents a rare opportunity for a variety of sophisticated technologies, such
as end-to-end cryptography and risk limiting audits, to be designed into a new
voting system, from scratch, with a variety of real world constraints, such as
election-day vote centers that must support thousands of ballot styles and run
all day in the event of a power failure.
We present and motivate the design of the STAR-Vote system, the benefits that we
expect from it, and its current status.
This is based on joint work with Josh Benaloh, Mike Byrne, Philip Kortum,
Neal McBurnett, Ron Rivest, Philip Stark, Dan Wallach
and the Office of the Travis County Clerk
I will show how families of concentrating stationary vortices for the shallow
water equations can be constructed and studied asymptotically. The main tool
is the study of asymptotics of solutions to a family of semilinear elliptic
problems. The same method also yields results for axisymmetric vortices for
the Euler equation of incompressible fluids.
In this talk we present different strategies for regularization of the pure Newton method
(minimization problems)and of the Gauss-Newton method (systems of nonlinear equations).
For these schemes, we prove general convergence results. We establish also the global and
local worst-case complexity bounds. It is shown that the corresponding search directions can
be computed by a standard linear algebra technique.
Graph-theoretic ideas have become very useful in understanding modern large-scale data-mining techniques. We show in this talk that ideas from optimization are also quite useful to better understand the numerical behavior of the corresponding algorithms. We illustrate this claim by looking at two specific graph theoretic problems and their application in data-mining.
The first problem is that of reputation systems where the reputation of objects and voters on the web are estimated; the second problem is that of estimating the similarity of nodes of large graphs. These two problems are also illustrated using concrete applications in data-mining.