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Since its invention in 2014, the Adam optimizer has received tremendous attention. On one hand, it has been widely used in deep learning and many variants have been proposed, while on the other hand their theoretical convergence property…

Machine Learning · Computer Science 2021-12-08 Zhishuai Guo , Yi Xu , Wotao Yin , Rong Jin , Tianbao Yang

We consider stochastic zero-order optimization problems, which arise in settings from simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients of a stochastic…

Optimization and Control · Mathematics 2019-10-31 Raghu Bollapragada , Stefan M. Wild

The aim of this paper is to propose an efficient adaptive finite element method for eigenvalue problems based on the multilevel correction scheme and inverse power method. This method involves solving associated boundary value problems on…

Numerical Analysis · Mathematics 2022-02-25 Qichen Hong , Hehu Xie , Fei Xu

In this article we prove that it is possible to construct, using newest-vertex bisection, meshes that equidistribute the error in $H^1$-norm, whenever the function to approximate can be decomposed as a sum of a regular part plus a singular…

Numerical Analysis · Mathematics 2008-03-28 Fernando D. Gaspoz , Pedro Morin

In this paper, we study an adaptive finite element method for a class of a nonlinear eigenvalue problems that may be of nonconvex energy functional and consider its applications to quantum chemistry. We prove the convergence of adaptive…

Numerical Analysis · Mathematics 2010-01-15 H. Chen , X. Gong , L. He , A. Zhou

We formulate and analyze a goal-oriented adaptive finite element method for a symmetric linear elliptic partial differential equation (PDE) that can simultaneously deal with multiple linear goal functionals. In each step of the algorithm,…

Numerical Analysis · Mathematics 2026-01-06 Roland Becker , Maximilian Brunner , Paula Hilbert , Michael Innerberger , Dirk Praetorius

We develop adaptive discretization algorithms for locally optimal experimental design of nonlinear prediction models. With these algorithms, we refine and improve a pertinent state-of-the-art algorithm in various respects. We establish…

Optimization and Control · Mathematics 2024-06-04 Jochen Schmid , Philipp Seufert , Michael Bortz

We consider a framework for the construction of iterative schemes for operator equations that combine low-rank approximation in tensor formats and adaptive approximation in a basis. Under fairly general assumptions, we obtain a rigorous…

Numerical Analysis · Mathematics 2014-03-17 Markus Bachmayr , Wolfgang Dahmen

We study the fundamental problem of selecting optimal features for model construction. This problem is computationally challenging on large datasets, even with the use of greedy algorithm variants. To address this challenge, we extend the…

We revisit adaptive time stepping, one of the classical topics of numerical analysis and computational engineering. While widely used in application and subject of many theoretical works, a complete understanding is still missing. Apart…

Numerical Analysis · Mathematics 2025-06-24 Michael Feischl , David Niederkofler

We analyze adaptive mesh-refining algorithms for conforming finite element discretizations of certain non-linear second-order partial differential equations. We allow continuous polynomials of arbitrary, but fixed polynomial order. The…

Numerical Analysis · Mathematics 2014-03-14 Michael Feischl , Thomas Führer , Dirk Praetorius

In this article we prove convergence of adaptive finite element methods for second order elliptic eigenvalue problems. We consider Lagrange finite elements of any degree and prove convergence for simple as well as multiple eigenvalues under…

Numerical Analysis · Mathematics 2008-03-05 Eduardo M. Garau , Pedro Morin , Carlos Zuppa

An accelerated class of adaptive scheme of iterative thresholding algorithms is studied analytically and empirically. They are based on the feedback mechanism of the null space tuning techniques (NST+HT+FB). The main contribution of this…

Information Theory · Computer Science 2020-05-15 Ningning Han , Shidong Li , Zhanjie Song

When data is collected in an adaptive manner, even simple methods like ordinary least squares can exhibit non-normal asymptotic behavior. As an undesirable consequence, hypothesis tests and confidence intervals based on asymptotic normality…

Statistics Theory · Mathematics 2023-03-23 Koulik Khamaru , Yash Deshpande , Tor Lattimore , Lester Mackey , Martin J. Wainwright

When solving elliptic partial differential equations in a region containing immersed interfaces (possibly evolving in time), it is often desirable to approximate the problem using an independent background discretisation, not aligned with…

Numerical Analysis · Mathematics 2019-10-29 Luca Heltai , Nella Rotundo

Partial differential equations (PDEs) with inputs that depend on infinitely many parameters pose serious theoretical and computational challenges. Sophisticated numerical algorithms that automatically determine which parameters need to be…

Numerical Analysis · Mathematics 2018-06-18 Adam J. Crowder , Catherine E. Powell , Alex Bespalov

Recent literature on online learning has focused on developing adaptive algorithms that take advantage of a regularity of the sequence of observations, yet retain worst-case performance guarantees. A complementary direction is to develop…

Machine Learning · Computer Science 2015-01-27 Ali Jadbabaie , Alexander Rakhlin , Shahin Shahrampour , Karthik Sridharan

Numerical computation of harmonic forms (typically called harmonic fields in three space dimensions) arises in various areas, including computer graphics and computational electromagnetics. The finite element exterior calculus framework…

Numerical Analysis · Mathematics 2016-06-24 Alan Demlow

In a recent work, we analyzed a weighted-residual error estimator for isogeometric boundary element methods in 2D and proposed an adaptive algorithm which steers the local mesh-refinement of the underlying partition as well as the…

Numerical Analysis · Mathematics 2017-06-13 Michael Feischl , Gregor Gantner , Alexander Haberl , Dirk Praetorius

We introduce the notion of consistent error bound functions which provides a unifying framework for error bounds for multiple convex sets. This framework goes beyond the classical Lipschitzian and H\"olderian error bounds and includes…

Optimization and Control · Mathematics 2023-10-20 Tianxiang Liu , Bruno F. Lourenço