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One of the main strengths of online algorithms is their ability to adapt to arbitrary data sequences. This is especially important in nonparametric settings, where performance is measured against rich classes of comparator functions that…

Machine Learning · Computer Science 2020-11-03 Ilja Kuzborskij , Nicolò Cesa-Bianchi

This paper studies the adversarial-robustness of importance-sampling (aka sensitivity sampling); a useful algorithmic technique that samples elements with probabilities proportional to some measure of their importance. A streaming or online…

Data Structures and Algorithms · Computer Science 2025-12-11 Yotam Kenneth-Mordoch , Shay Sapir

In this paper, we consider the generalized Forchheimer flows for slightly compressible fluids in porous media. Using Muskat's and Ward's general form of Forchheimer equations, we describe the flow of a single-phase fluid in $\mathbb R^d,…

Numerical Analysis · Mathematics 2016-06-13 Thinh Kieu

Adaptive optimization has achieved notable success for distributed learning while extending adaptive optimizer to federated Learning (FL) suffers from severe inefficiency, including (i) rugged convergence due to inaccurate gradient…

Machine Learning · Computer Science 2023-08-02 Yan Sun , Li Shen , Hao Sun , Liang Ding , Dacheng Tao

In this paper, we propose a deep-learning-based approach to a class of multiscale problems. THe Generalized Multiscale Finite Element Method (GMsFEM) has been proven successful as a model reduction technique of flow problems in…

Numerical Analysis · Mathematics 2018-10-30 Min Wang , Siu Wun Cheung , Eric T. Chung , Yalchin Efendiev , Wing Tat Leung , Yating Wang

Mortar methods are widely used techniques for discretizations of partial differential equations and preconditioners for the algebraic systems resulting from the discretizations. For problems with high contrast and multiple scales, the…

Numerical Analysis · Mathematics 2016-09-12 Eric T. Chung , Shubin Fu , Yanfang Yang

Diffusion models have recently shown promise in offline RL. However, these methods often suffer from high training costs and slow convergence, particularly when using transformer-based denoising backbones. While several optimization…

Machine Learning · Computer Science 2025-06-23 Zhiying Qiu , Tao Lin

This paper presents and analyses a Constraint Energy Minimization Generalized Multiscale Finite Element Method (CEM-GMsFEM) for solving single-phase non-linear compressible flows in highly heterogeneous media. The construction of CEM-GMsFEM…

Numerical Analysis · Mathematics 2023-03-31 Leonardo A. Poveda , Shubin Fu , Eric T. Chung , Lina Zhao

A novel finite element framework is proposed for the numerical simulation of two phase flows with surface tension. The Level-Set (LS) method with piece-wise quadratic (P2) interpolation for the liquid-gas interface is used in order to reach…

Computational Engineering, Finance, and Science · Computer Science 2020-10-27 Modesar Shakoor , Chung Hae Park

We investigate online convex optimization in non-stationary environments and choose dynamic regret as the performance measure, defined as the difference between cumulative loss incurred by the online algorithm and that of any feasible…

Machine Learning · Computer Science 2024-04-09 Peng Zhao , Yu-Jie Zhang , Lijun Zhang , Zhi-Hua Zhou

This research rigorously investigates the convergence of adaptive finite element methods for regularized variational models of quasi-static brittle fracture in elastic solids. We specifically examine a novel Ambrosio-Tortorelli (AT1)…

Numerical Analysis · Mathematics 2025-07-02 Ram Manohar , S. M. Mallikarjunaiah

We study offline-online reinforcement learning in linear mixture Markov decision processes (MDPs) under environment shift. In the offline phase, data are collected by an unknown behavior policy and may come from a mismatched environment,…

Machine Learning · Computer Science 2026-04-15 Zhongjun Zhang , Sean R. Sinclair

We propose a new randomized optimization method for high-dimensional problems which can be seen as a generalization of coordinate descent to random subspaces. We show that an adaptive sampling strategy for the random subspace significantly…

Optimization and Control · Mathematics 2019-12-19 Jonathan Lacotte , Mert Pilanci , Marco Pavone

A space-time domain decomposition approach is presented as a natural extension of the enhanced velocity mixed finite element (EVMFE) [Wheeler et. al] for spatial domain decomposition. The proposed approach allows for different space-time…

Numerical Analysis · Mathematics 2018-09-26 Gurpreet Singh , Mary F. Wheeler

In this paper, we systemically review and compare two mixed multiscale finite element methods (MMsFEM) for multiphase transport in highly heterogeneous media. In particular, we will consider the mixed multiscale finite element method using…

Numerical Analysis · Mathematics 2021-06-09 Yiran Wang , Eric Chung , Shubin Fu

Optimal-order uniform-in-time $H^1$-norm error estimates are given for semi- and full discretizations of mean curvature flow of surfaces in arbitrarily high codimension. The proposed and studied numerical method is based on a parabolic…

Numerical Analysis · Mathematics 2022-02-04 Tim Binz , Balázs Kovács

In this paper, we present a multiscale method for simulations of the multicontinua unsaturated flow problems in heterogeneous fractured porous media. The mathematical model is described by the system of Richards equations for each continuum…

Numerical Analysis · Mathematics 2019-08-02 Denis Spiridonov , Maria Vasilyeva , Eric T. Chung

A proof of convergence is given for a novel evolving surface finite element semi-discretization of Willmore flow of closed two-dimensional surfaces, and also of surface diffusion flow. The numerical method proposed and studied here…

Numerical Analysis · Mathematics 2020-07-31 Balázs Kovács , Buyang Li , Christian Lubich

We prove the convergence of an adaptive mixed finite element method (AMFEM) for (nonsymmetric) convection-diffusion-reaction equations. The convergence result holds from the cases where convection or reaction is not present to convection-or…

Numerical Analysis · Mathematics 2015-03-26 Shaohong Du , Xiaoping Xie

In this work, we build upon the recently introduced Multiscale Robin Coupled Method with Oversampling and Smoothing (MRCM-OS) to develop two highly efficient iterative multiscale methods. The MRCM-OS methodology demonstrated the ability to…

Numerical Analysis · Mathematics 2025-02-25 Dilong Zhou , Rafael T. Guiraldello , Felipe Pereira