2,484 research outputs found
Almost Sure Stabilization for Adaptive Controls of Regime-switching LQ Systems with A Hidden Markov Chain
This work is devoted to the almost sure stabilization of adaptive control
systems that involve an unknown Markov chain. The control system displays
continuous dynamics represented by differential equations and discrete events
given by a hidden Markov chain. Different from previous work on stabilization
of adaptive controlled systems with a hidden Markov chain, where average
criteria were considered, this work focuses on the almost sure stabilization or
sample path stabilization of the underlying processes. Under simple conditions,
it is shown that as long as the feedback controls have linear growth in the
continuous component, the resulting process is regular. Moreover, by
appropriate choice of the Lyapunov functions, it is shown that the adaptive
system is stabilizable almost surely. As a by-product, it is also established
that the controlled process is positive recurrent
Adaptive Finite Element Methods for Elliptic Problems with Discontinuous Coefficients
Elliptic partial differential equations (PDEs) with discontinuous diffusion
coefficients occur in application domains such as diffusions through porous
media, electro-magnetic field propagation on heterogeneous media, and diffusion
processes on rough surfaces. The standard approach to numerically treating such
problems using finite element methods is to assume that the discontinuities lie
on the boundaries of the cells in the initial triangulation. However, this does
not match applications where discontinuities occur on curves, surfaces, or
manifolds, and could even be unknown beforehand. One of the obstacles to
treating such discontinuity problems is that the usual perturbation theory for
elliptic PDEs assumes bounds for the distortion of the coefficients in the
norm and this in turn requires that the discontinuities are matched
exactly when the coefficients are approximated. We present a new approach based
on distortion of the coefficients in an norm with which
therefore does not require the exact matching of the discontinuities. We then
use this new distortion theory to formulate new adaptive finite element methods
(AFEMs) for such discontinuity problems. We show that such AFEMs are optimal in
the sense of distortion versus number of computations, and report insightful
numerical results supporting our analysis.Comment: 24 page
Generalized Weiszfeld algorithms for Lq optimization
In many computer vision applications, a desired model of some type is computed by minimizing a cost function based on several measurements. Typically, one may compute the model that minimizes the L₂ cost, that is the sum of squares of measurement errors with respect to the model. However, the Lq solution which minimizes the sum of the qth power of errors usually gives more robust results in the presence of outliers for some values of q, for example, q = 1. The Weiszfeld algorithm is a classic algorithm for finding the geometric L1 mean of a set of points in Euclidean space. It is provably optimal and requires neither differentiation, nor line search. The Weiszfeld algorithm has also been generalized to find the L1 mean of a set of points on a Riemannian manifold of non-negative curvature. This paper shows that the Weiszfeld approach may be extended to a wide variety of problems to find an Lq mean for 1 ≤ q <; 2, while maintaining simplicity and provable convergence. We apply this problem to both single-rotation averaging (under which the algorithm provably finds the global Lq optimum) and multiple rotation averaging (for which no such proof exists). Experimental results of Lq optimization for rotations show the improved reliability and robustness compared to L₂ optimization.This research has been funded by National ICT Australia
On the Multilinear Restriction and Kakeya conjectures
We prove -linear analogues of the classical restriction and Kakeya
conjectures in . Our approach involves obtaining monotonicity formulae
pertaining to a certain evolution of families of gaussians, closely related to
heat flow. We conclude by giving some applications to the corresponding
variable-coefficient problems and the so-called "joints" problem, as well as
presenting some -linear analogues for .Comment: 38 pages, no figures, submitte
Manifold interpolation and model reduction
One approach to parametric and adaptive model reduction is via the
interpolation of orthogonal bases, subspaces or positive definite system
matrices. In all these cases, the sampled inputs stem from matrix sets that
feature a geometric structure and thus form so-called matrix manifolds. This
work will be featured as a chapter in the upcoming Handbook on Model Order
Reduction (P. Benner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W.H.A.
Schilders, L.M. Silveira, eds, to appear on DE GRUYTER) and reviews the
numerical treatment of the most important matrix manifolds that arise in the
context of model reduction. Moreover, the principal approaches to data
interpolation and Taylor-like extrapolation on matrix manifolds are outlined
and complemented by algorithms in pseudo-code.Comment: 37 pages, 4 figures, featured chapter of upcoming "Handbook on Model
Order Reduction
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