English
Related papers

Related papers: Semiclassical resolvent estimates at trapped sets

200 papers

This work examines under what circumstances adaptivity for truncated SVD estimation can be achieved by an early stopping rule based on the smoothed residuals $ \| ( A A^{\top} )^{\alpha / 2} ( Y - A \hat{\mu}^{( m )}) \|^{2} $. Lower and…

Statistics Theory · Mathematics 2020-09-01 Bernhard Stankewitz

We assume the direct sum <A> o <B> for the signal subspace. As a result of post- measurement, a number of operational contexts presuppose the a priori knowledge of the LB -dimensional "interfering" subspace <B> and the goal is to estimate…

Applications · Statistics 2017-04-17 Guillaume Bouleux , Rémy Boyer

Our aim in this work is to give some quantitative insight on the dispersive effects exhibited by solutions of a semiclassical Schr{\"o}dinger-type equation in R d. We describe quantitatively the localisation of the energy in a long-time…

Analysis of PDEs · Mathematics 2018-03-26 Victor Chabu , Clotilde Fermanian-Kammerer , Fabricio Macià

We propose nonparametric identification and semiparametric estimation of joint potential outcome distributions in the presence of confounding. First, in settings with observed confounding, we derive tighter, covariate-informed bounds on the…

Methodology · Statistics 2026-02-19 Jianle Sun , Kun Zhang

Randomized higher-order computation can be seen as being captured by a lambda calculus endowed with a single algebraic operation, namely a construct for binary probabilistic choice. What matters about such computations is the probability of…

Logic in Computer Science · Computer Science 2020-12-24 Ugo Dal Lago , Claudia Faggian , Simona Ronchi Della Rocca

This paper studies semiparametric contextual bandits, a generalization of the linear stochastic bandit problem where the reward for an action is modeled as a linear function of known action features confounded by an non-linear…

Machine Learning · Statistics 2018-07-17 Akshay Krishnamurthy , Zhiwei Steven Wu , Vasilis Syrgkanis

We show that a wide class of geometrically defined overdetermined semilinear partial differential equations may be explicitly prolonged to obtain closed systems. As a consequence, in the case of linear equations we extract sharp bounds on…

Differential Geometry · Mathematics 2008-11-26 Thomas Branson , Andreas Cap , Michael Eastwood , Rod Gover

The purpose of this paper is to revisit the proof of the Gearhart-Pr\"uss-Huang-Greiner theorem for a semigroup $S(t)$, following the general idea of the proofs that we have seen in the literature and to get an explicit estimate on the…

Functional Analysis · Mathematics 2023-03-23 Bernard Helffer , Johannes Sjöstrand , Joe Viola

Resolvents of set-valued operators play a central role in various branches of mathematics and in particular in the design and the analysis of splitting algorithms for solving monotone inclusions. We propose a generalization of this notion,…

Optimization and Control · Mathematics 2020-06-24 Minh N. Bùi , Patrick L. Combettes

We prove Strichartz estimates on general flat d-torus for arbitrary d. Using these estimates, we prove local wellposedness for the cubic nonlinear Schr\"odinger equations in appropriate Sobolev spaces. In dimensions 2 and 3, we prove…

Analysis of PDEs · Mathematics 2008-09-29 F. Catoire , W. -M. Wang

Schr\"odinger equations with nonlinearities concentrated in some regions of space are good models of various physical situations and have interesting mathematical properties. We show that in the semiclassical limit it is possible to…

Condensed Matter · Physics 2015-06-25 Giovanni Jona-Lasinio , Carlo Presilla , Johannes Sjöstrand

In this paper, we propose some new semidefinite relaxations for a class of nonconvex complex quadratic programming problems, which widely appear in the areas of signal processing and power system. By deriving new valid constraints to the…

Optimization and Control · Mathematics 2023-05-18 Yingzhe Xu , Cheng Lu , Zhibin Deng , Ya-Feng Liu

The purpose of this article is to develop a general parametric estimation theory that allows the derivation of the limit distribution of estimators in non-regular models where the true parameter value may lie on the boundary of the…

Statistics Theory · Mathematics 2022-11-28 Junichiro Yoshida , Nakahiro Yoshida

The article addresses the convergence of implicit and semi-implicit, fully discrete approximations of a class of nonlinear parabolic evolution problems. Such schemes are popular in the numerical solution of evolutions defined with the…

Numerical Analysis · Mathematics 2019-02-22 Sören Bartels , Michael Růžička

The purpose of this paper is to revisit the proof of the Gearhardt-Pr\"uss-Hwang-Greiner theorem for a semigroup $S(t)$, following the general idea of the proofs that we have seen in the literature and to get an explicit estimate on $\Vert…

Optimization and Control · Mathematics 2021-03-12 Bernard Helffer , Johannes Sjöstrand

In this paper, we develop a general theory of truncated inverse binomial sampling. In this theory, the fixed-size sampling and inverse binomial sampling are accommodated as special cases. In particular, the classical Chernoff-Hoeffding…

Statistics Theory · Mathematics 2019-08-20 Xinjia Chen

For a class of non compact Riemannian manifolds with ends, we give pseudo-differential expansions of bounded functions of the semi-classical Laplacian and study related Lp boundedness properties.

Analysis of PDEs · Mathematics 2007-11-26 Jean-Marc Bouclet

We describe the general qualitative behaviour of the resolvent norm for a very wide class of non-self-adjoint Schroedinger operators in the semi-classical regime, as the spectral parameter varies over the complex plane.

Spectral Theory · Mathematics 2007-05-23 Paul Redparth

We investigate how to learn treatment effects away from the cutoff in multiple-cutoff regression discontinuity designs. Using a microeconomic model, we demonstrate that the parallel-trend type assumption proposed in the literature is…

Econometrics · Economics 2025-09-03 Yuta Okamoto , Yuuki Ozaki

We present a transductive learning algorithm that takes as input training examples from a distribution $P$ and arbitrary (unlabeled) test examples, possibly chosen by an adversary. This is unlike prior work that assumes that test examples…

Machine Learning · Computer Science 2020-10-01 Shafi Goldwasser , Adam Tauman Kalai , Yael Tauman Kalai , Omar Montasser