This talk attempts to survey key aspects of the mathematics that has been developed in recent years towards an explicit understanding of the structure of exponential functionals of Brownian motion, starting with work of Yor's in the 1990s
It is known that a continuous path of bounded variation
can be reconstructed from a sequence of its iterated integrals (called the signature) in a similar way to a function on the circle being reconstructed from its Fourier coefficients. We study the radius of convergence of the corresponding logarithmic signature for paths in an arbitrary Banach space. This convergence has important consequences for control theory (in particular, it can be used for computing the logarithm of a flow)and the efficiency of numerical approximations to solutions of SDEs. We also discuss the nonlinear structure of the space of logarithmic signatures and the problem of reconstructing a path by its signature.
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The convergence of iterative methods for solving the linear system Ax = b with a Hermitian positive definite matrix A depends on the condition number of A: the smaller the latter the faster the former. Hence the idea to multiply the equation by a matrix T such that the condition number of TA is much smaller than that of A. The above is a common interpretation of the technique known as preconditioning, the matrix T being referred to as the preconditioner for A.
The eigenvalue computation does not seem to benefit from the direct application of such a technique. Indeed, what is the point in replacing the standard eigenvalue problem Ax = λx with the generalized one TAx = λTx that does not appear to be any easier to solve? It is hardly surprising then that modern eigensolvers, such as ARPACK, do not use preconditioning directly. Instead, an option is provided to accelerate the convergence to the sought eigenpairs by applying spectral transformation, which generally requires the user to supply a subroutine that solves the system (A−σI)y = z, and it is entirely up to the user to employ preconditioning if they opt to solve this system iteratively.
In this talk we discuss some alternative views on the preconditioning technique that are more general and more useful in the convergence analysis of iterative methods and that show, in particular, that the direct preconditioning approach does make sense in eigenvalue computation. We review some iterative algorithms that can benefit from the direct preconditioning, present available convergence results and demonstrate both theoretically and numerically that the direct preconditioning approach has advantages over the two-level approach. Finally, we discuss the role that preconditioning can play in the a posteriori error analysis, present some a posteriori error estimates that use preconditioning and compare them with commonly used estimates in terms of the Euclidean norm of residual.
We introduce conditions in the spirit of $(T)$ and $(T')$ of the discrete setting, that imply, when $d \geq 2$, a law of large numbers with non-vanishing limiting velocity (which we refer to as 'ballistic behavior') and a central limit theorem with non-degenerate covariance matrix. As an application of our results, we consider the class of diffusions where the diffusion matrix is the identity, and give a concrete criterion on the drift term under which the diffusion in random environment exhibits ballistic behavior.
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We consider the problem of computing ratings using the results of games (such as chess) played between a set of n players, and show how this problem can be reduced to computing the positive eigenvectors corresponding to the dominant eigenvalues of certain n by n matrices. There is a close connection with the stationary probability distributions of certain Markov chains. In practice, if n is large, then the matrices involved will be sparse, and the power method may be used to solve the eigenvalue problems efficiently.
It is well-known that the only space-time scaling limits of Galton-Watson processes are continuous-state branching processes. Their genealogical structure is most explicitly expressed by discrete trees and R-trees, respectively. Weak limit theorems have been recently established for some of these random trees. We study here a Markovian forest growth procedure that allows to construct the genealogical forest of any continuous-state branching process with immigration as an a.s. limit of Galton-Watson forests with edge lengths. Furthermore, we are naturally led to continuous forests with edge lengths. Another strength of our method is that it yields results in the general supercritical case that was excluded in most of the previous literature.
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We discuss the application of gradient methods to calibrate mean reverting
stochastic volatility models. For this we use formulas based on Girsanov
transformations as well as a modification of the Bismut-Elworthy formula to
compute the derivatives of certain option prices with respect to the
parameters of the model by applying Monte Carlo methods. The article
presents an extension of the ideas to apply Malliavin calculus methods in
the computation of Greek's.
Many industrial flow problems, expecially in the minerals and process
industries, are very complex, with strong interactions between phases
and components, and with very different length and time scales. This
presentation outlines the algorithms used in the CFX-5 software, and
describes the extension of its coupled solver approach to some
multi-scale industrial problems. including Population Balance modelling
to predict size distributions of a disperse phase. These results will be
illustrated on some practical industrial problems.