Bounds on least dilatations
نویسندگان
چکیده
منابع مشابه
The asymptotic behavior of least pseudo-Anosov dilatations
Let S = Sg,n be an orientable surface with genus g and n marked points. The mapping class group of S is defined to be the group of homotopy classes of orientation preserving homeomorphisms of S. We denote it by Mod(S). Given a pseudo-Anosov element f ∈ Mod(S), let λ(f ) denote the dilatation of f (see section 2.1). We define L(Sg,n) := {log λ(f )|f ∈ Mod(Sg,n) pseudo-Anosov}. This is precisely ...
متن کاملNew Bounds on Compressive Linear Least Squares Regression
In this paper we provide a new analysis of compressive least squares regression that removes a spurious logN factor from previous bounds, where N is the number of training points. Our new bound has a clear interpretation and reveals meaningful structural properties of the linear regression problem that makes it solvable effectively in a small dimensional random subspace. In addition, the main p...
متن کاملBounds on the Minimizers of (nonconvex) Regularized Least-Squares
This is a theoretical study on the minimizers of cost-functions composed of an `2 data-fidelity term and a possibly nonsmooth or nonconvex regularization term acting on the differences or the discrete gradients of the image or the signal to restore. More precisely, we derive general nonasymptotic analytical bounds characterizing the local and the global minimizers of these cost-functions. We fi...
متن کاملOn Some Asymptotic Uncertainty Bounds in Recursive Least Squares Identiication
This paper deals with the performance of the recursive least squares algorithm when it is applied to problems where the measured signal is corrupted by bounded noise. Using ideas from bounding ellipsoid algorithms we derive an asymptotic expression for the bound on the uncertainty of the parameter estimate for a simple choice of design variables. This bound is also transformed to a bound on the...
متن کاملH∞ bounds for least-squares estimators
In this paper we obtain upper and lower bounds for the H 1 norm of the Kalman lter and RLS algorithm, with respect to prediction and ltered errors. These bounds can be used to study the robustness properties of such estimators. One main conclusion is that, unlike H 1-optimal estimators which do not allow for any ampliication of the disturbances, the least-squares estimators do allow for such am...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the American Mathematical Society
سال: 1991
ISSN: 0002-9939
DOI: 10.1090/s0002-9939-1991-1068128-8