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A common belief in high-dimensional data analysis is that data are concentrated on a low-dimensional manifold. This motivates simultaneous dimension reduction and regression on manifolds. We provide an algorithm for learning gradients on…

Statistics Theory · Mathematics 2010-02-24 Sayan Mukherjee , Qiang Wu , Ding-Xuan Zhou

High-dimensional data analysis has been an active area, and the main focuses have been variable selection and dimension reduction. In practice, it occurs often that the variables are located on an unknown, lower-dimensional nonlinear…

Statistics Theory · Mathematics 2012-07-31 Ming-Yen Cheng , Hau-tieng Wu

This chapter makes a review, in a complete methodological framework, of various global sensitivity analysis methods of model output. Numerous statistical and probabilistic tools (regression, smoothing, tests, statistical learning, Monte…

Statistics Theory · Mathematics 2014-04-10 Bertrand Iooss , Paul Lemaître

We present a generalization of quantum error correction to infinite-dimensional Hilbert spaces. The generalization yields new classes of quantum error correcting codes that have no finite-dimensional counterparts. The error correction…

Quantum Physics · Physics 2009-07-06 Cédric Bény , Achim Kempf , David W. Kribs

This paper deals with the polynomial linear system solving with errors (PLSwE) problem. Specifically, we focus on the evaluation-interpolation technique for solving polynomial linear systems and we assume that errors can occur in the…

Symbolic Computation · Computer Science 2021-02-09 Guerrini Eleonora , Lebreton Romain , Zappatore Ilaria

This work provides a nonasymptotic error analysis of quantum Krylov algorithms based on real-time evolutions, subject to generic errors in the outputs of the quantum circuits. We prove upper and lower bounds on the resulting ground state…

Quantum Physics · Physics 2024-09-04 William Kirby

This work develops novel error expansions with computable leading order terms for the global weak error in the tau-leap discretization of pure jump processes arising in kinetic Monte Carlo models. Accurate computable a posteriori error…

Numerical Analysis · Mathematics 2011-10-21 Jesper Karlsson , Raul Tempone

The aim of this paper is to implement some new techniques, based on conjugate duality in convex optimization, for proving the existence of global error bounds for convex inequality systems. We deal first of all with systems described via…

Optimization and Control · Mathematics 2010-07-13 Radu Ioan Bot , Ernö Robert Csetnek

We prove sharp, computable error estimates for the propagation of errors in the numerical solution of ordinary differential equations. The new estimates extend previous estimates of the influence of data errors and discretisation errors…

Numerical Analysis · Mathematics 2015-04-28 Benjamin Kehlet , Anders Logg

Initial-boundary value problems in a strip with different types of boundary conditions for two-dimensional generalized Zakharov--Kuznetsov equation are considered. Results on global existence and uniqueness of weak solutions in certain…

Analysis of PDEs · Mathematics 2013-01-16 Andrei Faminskii , Evgeniya Baykova

In this paper we focus on the initial value problem for quasi-linear dissipative plate equation in multi-dimensional space $(n\geq2)$. This equation verifies the decay property of the regularity-loss type, which causes the difficulty in…

Analysis of PDEs · Mathematics 2010-03-16 Yongqin Liu , Shuichi Kawashima

Due to the increasing demand for high performance and cost reduction within the framework of complex system design, numerical optimization of computationally costly problems is an increasingly popular topic in most engineering fields. In…

Optimization and Control · Mathematics 2018-06-12 Julien Pelamatti , Loïc Brevault , Mathieu Balesdent , El-Ghazali Talbi , Yannick Guerin

The gradient discretisation method (GDM) is a generic framework designed recently, as a discretise in spatial space, to partial differential equations. This paper aims to use the GDM to establish a first general error estimate for numerical…

Numerical Analysis · Mathematics 2020-09-22 Yahya Alnashri

Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, needs to consider four aspects: the norm, the loss function, the design matrix, and the noise model. This…

Machine Learning · Statistics 2015-12-01 Arindam Banerjee , Sheng Chen , Farideh Fazayeli , Vidyashankar Sivakumar

We consider a general linear parabolic problem with extended time boundary conditions (including initial value problems and periodic ones), and approximate it by the implicit Euler scheme in time and the Gradient Discretisation method in…

Numerical Analysis · Mathematics 2023-08-22 J Droniou , R Eymard , T Gallouët , C Guichard , R Herbin

Finding global optima in high-dimensional optimization problems is extremely challenging since the number of function evaluations required to sufficiently explore the search space increases exponentially with its dimensionality.…

Machine Learning · Computer Science 2022-11-04 Julian F. Schumann , Alejandro M. Aragón

The effectiveness of using model sparsity as a priori information when solving linear inverse problems is studied. We investigate the reconstruction quality of such a method in the non-idealized case and compute some typical recovery errors…

Numerical Analysis · Mathematics 2010-07-19 Ignace Loris , Caroline Verhoeven

The Richards equation is commonly used to model the flow of water and air through soil, and it serves as a gateway equation for multiphase flows through porous media. It is a nonlinear advection-reaction-diffusion equation that exhibits…

Numerical Analysis · Mathematics 2021-08-31 K. Mitra , M. Vohralík

This article describes the extension of recent methods for a posteriori error estimation such as dual-weighted residual methods to node-centered finite volume discretizations of second order elliptic boundary value problems including upwind…

Numerical Analysis · Mathematics 2026-02-04 Lutz Angermann

This paper is concerned with goal-oriented a posteriori error estimation for nonlinear functionals in the context of nonlinear variational problems solved with continuous Galerkin finite element discretizations. A two-level, or discrete,…

Computational Engineering, Finance, and Science · Computer Science 2025-01-10 Brian N. Granzow , D. Thomas Seidl , Stephen D. Bond
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