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When performing a Monte Carlo calculation, the running time should in principle be much longer than the autocorrelation time in order to get reliable results. Among different lattice fermion models, the Holstein model is notorious for its…

Strongly Correlated Electrons · Physics 2021-08-18 Meng Yao , Da Wang , Qiang-Hua Wang

This paper shows that the endogeneity test using the control function approach in linear instrumental variable models is a variant of the Hausman test. Moreover, we find that the test statistics used in these tests can be numerically…

Econometrics · Economics 2023-12-19 Jinyong Hahn , Zhipeng Liao , Nan Liu , Shuyang Sheng

We address the problem of computing a control for a time-dependent nonlinear system to reach a target set in a minimal time. To solve this minimal time control problem, we introduce a hierarchy of linear semi-infinite programs, the values…

Optimization and Control · Mathematics 2023-07-04 Antoine Oustry , Matteo Tacchi

For modeling multivariate financial time series we propose a single factor copula model together with stochastic volatility margins. This model generalizes single factor models relying on the multivariate normal distribution and allows for…

Computation · Statistics 2019-07-22 Alexander Kreuzer , Claudia Czado

We present a practical algorithm based on symplectic splitting methods to integrate numerically in time the Schr\"odinger equation. When discretized in space, the Schr\"odinger equation can be recast as a classical Hamiltonian system…

Numerical Analysis · Mathematics 2015-02-24 S. Blanes , F. Casas , A. Murua

Linear autoregressive models serve as basic representations of discrete time stochastic processes. Different attempts have been made to provide non-linear versions of the basic autoregressive process, including different versions based on…

Machine Learning · Statistics 2016-03-17 Edgar A. Valencia , Mauricio A. Álvarez

Harmonic model predictive control (HMPC) is a model predictive control (MPC) formulation which displays several benefits over other MPC formulations, especially when using a small prediction horizon. These benefits, however, come at the…

Optimization and Control · Mathematics 2022-11-16 Pablo Krupa , Daniel Limon , Alberto Bemporad , Teodoro Alamo

We revisit the method of Carleman linearization for systems of ordinary differential equations with polynomial right-hand sides. This transformation provides an approximate linearization in a higher-dimensional space through the exact…

Numerical Analysis · Mathematics 2017-11-08 Marcelo Forets , Amaury Pouly

The linear-time ham-sandwich cut algorithm of Lo, Matou\v{s}ek, and Steiger for bi-chromatic finite point sets in the plane works by appropriately selecting crossings of the lines in the dual line arrangement with a set of well-chosen…

Computational Geometry · Computer Science 2015-03-11 Stefan Felsner , Alexander Pilz

We study the shortcut Fr\'{e}chet distance, a natural variant of the Fr\'{e}chet distance, that allows us to take shortcuts from and to any point along one of the curves. The classic Fr\'echet distance is a bottle-neck distance measure and…

Computational Geometry · Computer Science 2013-12-05 Maike Buchin , Anne Driemel , Bettina Speckmann

In this paper an easy to implement method of stochastically weighing short and long memory linear processes is introduced. The method renders asymptotically exact size confidence intervals for the population mean which are significantly…

Methodology · Statistics 2019-01-15 Masoud M Nasari , Mohamedou Ould-Haye

Shortcuts to adiabaticity are strategies for conserving adiabatic invariants under non-adiabatic (i.e. fast-driving) conditions. Here, we show how to extend classical, Hamiltonian shortcuts to adiabaticity to allow the crossing of a…

Statistical Mechanics · Physics 2024-08-14 Roi Holtzman , Oren Raz , Christopher Jarzynski

Consider the problem of testing $s$ hypotheses simultaneously. The usual approach restricts attention to procedures that control the probability of even one false rejection, the familywise error rate (FWER). If $s$ is large, one might be…

Statistics Theory · Mathematics 2007-11-06 Joseph P. Romano , Michael Wolf

We present a novel method for controlling the $k$-familywise error rate ($k$-FWER) in the linear regression setting using the knockoffs framework first introduced by Barber and Cand\`es. Our procedure, which we also refer to as knockoffs,…

Methodology · Statistics 2015-11-10 Lucas Janson , Weijie Su

We provide a systematic comparison of two numerical methods to solve the widely used nonlinear Schr\"odinger equation. The first one is the standard second order split-step (SS2) method based on operator splitting approach. The second one…

Computational Physics · Physics 2021-09-28 Anastassiya Semenova , Sergey A. Dyachenko , Alexander O. Korotkevich , Pavel M. Lushnikov

In this paper, we initiate a systematic investigation of differentially private algorithms for convex empirical risk minimization. Various instantiations of this problem have been studied before. We provide new algorithms and matching lower…

Machine Learning · Computer Science 2014-10-21 Raef Bassily , Adam Smith , Abhradeep Thakurta

In this paper, we propose a second-order energy-conserving approximation procedure for Hamiltonian systems with holonomic constraints. The derivation of the procedure relies on the use of the so-called line integral framework. We provide…

Numerical Analysis · Mathematics 2018-01-09 Luigi Brugnano , Gianmarco Gurioli , Felice Iavernaro , Ewa B. Weinmueller

This paper examines linear binary codes capable of correcting one or more errors. For the single-error-correcting case, it is shown that the Hamming bound is achieved by a constructive method, and an exact expression for the minimal…

Information Theory · Computer Science 2025-12-16 Timofei Izhitskii

Model checking has been successfully used in many computer science fields, including artificial intelligence, theoretical computer science, and databases. Most of the proposed solutions make use of classical, point-based temporal logics,…

Logic in Computer Science · Computer Science 2016-01-25 A. Molinari , A. Montanari , A. Peron

This paper presents a novel method for polynomial approximation (Hermite approximation) using the fusion of value and derivative information. Therefore, the least-squares error in both domains is simultaneously minimized. A covariance…

Numerical Analysis · Mathematics 2019-03-27 Roland Ritt , Matthew Harker , Paul O'Leary