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Constrained optimization problems where both the objective and constraints may be nonsmooth and nonconvex arise across many learning and data science settings. In this paper, we show for any Lipschitz, weakly convex objectives and…

Optimization and Control · Mathematics 2025-01-17 Zhichao Jia , Benjamin Grimmer

Many recent studies on first-order methods (FOMs) focus on \emph{composite non-convex non-smooth} optimization with linear and/or nonlinear function constraints. Upper (or worst-case) complexity bounds have been established for these…

Optimization and Control · Mathematics 2023-07-18 Wei Liu , Qihang Lin , Yangyang Xu

Recently, Arjevani et al. [1] established a lower bound of iteration complexity for the first-order optimization under an $L$-smooth condition and a bounded noise variance assumption. However, a thorough review of existing literature on…

Machine Learning · Computer Science 2023-10-30 Bohan Wang , Jingwen Fu , Huishuai Zhang , Nanning Zheng , Wei Chen

When solving the American options with or without dividends, numerical methods often obtain lower convergence rates if further treatment is not implemented even using high-order schemes. In this article, we present a fast and explicit…

Computational Finance · Quantitative Finance 2022-04-14 Chinonso Nwankwo , Weizhong Dai

The simplex method for linear programming is known to be highly efficient in practice, and understanding its performance from a theoretical perspective is an active research topic. The framework of smoothed analysis, first introduced by…

Data Structures and Algorithms · Computer Science 2025-10-22 Sophie Huiberts , Yin Tat Lee , Xinzhi Zhang

We prove lower bounds for higher-order methods in smooth non-convex finite-sum optimization. Our contribution is threefold: We first show that a deterministic algorithm cannot profit from the finite-sum structure of the objective, and that…

Optimization and Control · Mathematics 2021-07-05 Nicolas Emmenegger , Rasmus Kyng , Ahad N. Zehmakan

In this work we explore the fidelity of numerical approximations to the analytic spectra of hyperbolic partial differential equation systems with variable coefficients. We are particularly interested in the ability of discrete methods to…

Numerical Analysis · Mathematics 2025-08-12 Brittany A. Erickson

We establish rigorous \emph{a posteriori} error bounds for a space-time finite element method of arbitrary order discretising linear wave problems in second order formulation. The method combines standard finite elements in space and…

Numerical Analysis · Mathematics 2026-04-24 Zhaonan Dong , Emmanuil H. Georgoulis , Lorenzo Mascotto , Zuodong Wang

The problem of estimating the initial state of 1-D wave equations with globally Lipschitz nonlinearities from boundary measurements on a finite interval was solved recently by using the sequence of forward and backward observers, and…

Systems and Control · Computer Science 2016-02-25 Emilia Fridman , Maria Terushkin

We prove upper bounds on the order of convergence of Frolov's cubature formula for numerical integration in function spaces of dominating mixed smoothness on the unit cube with homogeneous boundary condition. More precisely, we study…

Numerical Analysis · Mathematics 2016-04-07 Mario Ullrich , Tino Ullrich

We consider a one-dimensional singularly perturbed 4th order problem with the additional feature of a shift term. An expansion into a smooth term, boundary layers and an inner layer yields a formal solution decomposition, and together with…

Numerical Analysis · Mathematics 2023-09-22 Sebastian Franz , Kleio Liotati

We study the optimal lower and upper complexity bounds for finding approximate solutions to the composite problem $\min_x\ f(x)+h(Ax-b)$, where $f$ is smooth and $h$ is convex. Given access to the proximal operator of $h$, for strongly…

Optimization and Control · Mathematics 2023-08-15 Zhenyuan Zhu , Fan Chen , Junyu Zhang , Zaiwen Wen

We establish lower bounds on the complexity of finding $\epsilon$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. We prove that deterministic first-order methods, even applied to arbitrarily…

Optimization and Control · Mathematics 2017-11-03 Yair Carmon , John C. Duchi , Oliver Hinder , Aaron Sidford

We introduce a new class of "filtered" schemes for some first order non-linear Hamilton-Jacobi-Bellman equations. The work follows recent ideas of Froese and Oberman (SIAM J. Numer. Anal., Vol 51, pp.423-444, 2013). The proposed schemes are…

Numerical Analysis · Mathematics 2016-02-19 Olivier Bokanowski , Maurizio Falcone , Smita Sahu

It has now become customary in the field of numerical relativity to couple high order finite difference schemes to mesh refinement algorithms. To this end, different modifications to the standard Berger-Oliger adaptive mesh refinement…

General Relativity and Quantum Cosmology · Physics 2015-04-29 Bishop Mongwane

We introduce the concept of strong high-order approximate minimizers for nonconvex optimization problems. These apply in both standard smooth and composite non-smooth settings, and additionally allow convex or inexpensive constraints. An…

Optimization and Control · Mathematics 2020-01-30 Coralia Cartis , Nick Gould , Philippe L. Toint

To improve convergence results obtained using a framework for unsymmetric meshless methods due to Schaback (Preprint G\"ottingen 2006), we extend, in two directions, the Sobolev bound due to Arcang\'eli et al. (Numer Math 107, 181-211,…

Numerical Analysis · Mathematics 2009-05-14 Andrew Corrigan , John Wallin , Thomas Wanner

A high order cut finite element method is formulated for solving the elastic wave equation. Both a single domain problem and an interface problem are treated. The boundary or interface are allowed to cut through the background mesh. To…

Numerical Analysis · Mathematics 2018-04-03 Simon Sticko , Gustav Ludvigsson , Gunilla Kreiss

It is natural to expect the following loosely stated approximation principle to hold: a numerical approximation solution should be in some sense as smooth as its target exact solution in order to have optimal convergence. For piecewise…

Numerical Analysis · Mathematics 2013-12-25 So-Hsiang Chou

The goal of ordinal embedding is to represent items as points in a low-dimensional Euclidean space given a set of constraints in the form of distance comparisons like "item $i$ is closer to item $j$ than item $k$". Ordinal constraints like…

Machine Learning · Statistics 2016-06-24 Lalit Jain , Kevin Jamieson , Robert Nowak