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Stochastic gradient descent (SGD) is a foundational algorithm for large-scale statistical learning and stochastic optimization. However, statistical inference based on SGD iterates remains challenging when stochastic gradients have infinite…

Machine Learning · Statistics 2026-05-26 Jose Blanchet , Peter Glynn , Wenhao Yang

The construction of high order entropy stable collocation schemes on quadrilateral and hexahedral elements has relied on the use of Gauss-Legendre-Lobatto collocation points and their equivalence with summation-by-parts (SBP) finite…

Numerical Analysis · Mathematics 2019-08-06 Jesse Chan , David C. Del Rey Fernandez , Mark H. Carpenter

This paper presents a pseudo-spectral method for Dynamic Optimization Problems (DOPs) that allows for tight polynomial bounds to be achieved via flexible sub-intervals. The proposed method not only rigorously enforces inequality…

Optimization and Control · Mathematics 2026-04-08 Eduardo M. G. Vila , Eric C. Kerrigan , Paul Bruce

Block coordinate descent methods and stochastic subgradient methods have been extensively studied in optimization and machine learning. By combining randomized block sampling with stochastic subgradient methods based on dual averaging, we…

Optimization and Control · Mathematics 2015-09-16 Qi Deng , Guanghui Lan , Anand Rangarajan

We study a new aggregation operator for gradients coming from a mini-batch for stochastic gradient (SG) methods that allows a significant speed-up in the case of sparse optimization problems. We call this method AdaBatch and it only…

Machine Learning · Computer Science 2017-11-07 Alexandre Défossez , Francis Bach

An initial-boundary value problem for the $n$-dimensional ($n\geq 2$) time-dependent Schr\"odinger equation in a semi-infinite (or infinite) parallelepiped is considered. Starting from the Numerov-Crank-Nicolson finite-difference scheme, we…

Numerical Analysis · Mathematics 2026-01-05 Bernard Ducomet , Alexander Zlotnik , Alla Romanova

This paper presents a high-order accurate Continuous Galerkin Finite Element Method (CGFEM) for solving the initial boundary value problems governed by the Incompressible Navier-Stokes (INS) equations. We discretize the INS equations using…

Numerical Analysis · Mathematics 2026-04-27 Mrityunjoy Mandal , Arnaud G Malan , Prince Nchupang , Jan Nordström

Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for general graphs. In this paper, we propose a novel…

Machine Learning · Computer Science 2015-01-06 Qixing Huang , Yuxin Chen , Leonidas Guibas

This study proposes an adaptive subsystem-based control (SBC) for systematic and straightforward nonlinear~control of nth-order strict-feedback form (SFF) systems.~By decomposing the SFF system to subsystems, a generic~term (namely…

Optimization and Control · Mathematics 2024-07-16 Janne Koivumäki , Jukka-Pekka Humaloja , Lassi Paunonen , Wen-Hong Zhu , Jouni Mattila

High-order numerical methods for conservation laws are highly sought after due to their potential efficiency. However, it is challenging to ensure their robustness, particularly for under-resolved flows. Baseline high-order methods often…

We demonstrate that we can carry over the strategy of Finite Element Exterior Calculus (FEEC) to Summation-by-Parts (SBP) Finite Difference (FD) methods to achieve divergence- and curl-free discretizations. This is not obvious at first…

A nonsmooth extension of the speed-gradient algorithms in finite form is proposed. The conditions ensuring control goal (convergence of the goal function to zero) are established. A new algorithm is applied to almost global stabilization of…

Optimization and Control · Mathematics 2018-10-03 M. V. Dolgopolik , A. L. Fradkov

Stability and error analysis of a hybridized discontinuous Galerkin finite element method for Stokes equations is presented. The method is locally conservative, and for particular choices of spaces the velocity field is point-wise…

Numerical Analysis · Mathematics 2018-02-01 Sander Rhebergen , Garth N. Wells

We present a time domain method to solve quantum scattering by an arbitrary potential of finite range. The scattering wave function in full space can be obtained, including the near field, the mid field (i.e. Fresnel region) and the far…

Quantum Physics · Physics 2024-12-31 Kun Chen

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (\textit{SDP}), which is generally…

Machine Learning · Computer Science 2019-12-03 Ke Ma , Jinshan Zeng , Qianqian Xu , Xiaochun Cao , Wei Liu , Yuan Yao

We propose a new system identification method, called Sign-Perturbed Sums (SPS), for constructing non-asymptotic confidence regions under mild statistical assumptions. SPS is introduced for linear regression models, including but not…

Signal Processing · Electrical Eng. & Systems 2018-07-24 Balázs Cs. Csáji , Marco C. Campi , Erik Weyer

The increasing penetration of renewable energy resources and utilization of energy storage systems pose new challenges in maintaining power system's stability. Specifically, the cost function of regulation no longer remains smooth, which…

Optimization and Control · Mathematics 2023-11-27 Yifan Wang , Shuai Liu , Xianghui Cao , Mo-Yuen Chow

In this paper, the stabilized finite element approximation of the Stokes eigenvalue problems is considered for both the two-field (displacement-pressure) and the three-field (stress-displacement-pressure) formulations. The method presented…

Numerical Analysis · Mathematics 2016-09-21 Önder Türk , Daniele Boffi , Ramon Codina

We construct a family of embedded pairs for optimal strong stability preserving explicit Runge-Kutta methods of order $2 \leq p \leq 4$ to be used to obtain numerical solution of spatially discretized hyperbolic PDEs. In this construction,…

Numerical Analysis · Mathematics 2022-05-17 Sidafa Conde , Imre Fekete , John N. Shadid

This paper introduces the localized sparsifying preconditioner for the pseudospectral approximations of indefinite systems on periodic structures. The work is built on top of the recently proposed sparsifying preconditioner with two major…

Numerical Analysis · Mathematics 2017-05-22 Fei Liu , Lexing Ying
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