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In this work, we present a comprehensive framework for approximating the weakly singular power-law kernel $t^{\alpha-1}$ of fractional integral and differential operators, where $\alpha \in (0,1)$ and $t \in [\delta,T]$ with…

Numerical Analysis · Mathematics 2025-08-29 Renu Chaudhary , Kai Diethelm , Afshin Farhadi , Fred A. Fuchs

We present a methodology for bounding the error term of an asymptotic solution to a singularly perturbed optimal control (SPOC) problem whose exact solution is known to be computationally intractable. In previous works, reduced or…

Optimization and Control · Mathematics 2016-10-20 Sei Howe , Panos Parpas

We propose improved fixed-design confidence bounds for the linear logistic model. Our bounds significantly improve upon the state-of-the-art bound by Li et al. (2017) via recent developments of the self-concordant analysis of the logistic…

Machine Learning · Statistics 2022-06-22 Kwang-Sung Jun , Lalit Jain , Blake Mason , Houssam Nassif

We investigate error bounds for numerical solutions of divergence structure linear elliptic PDEs on compact manifolds without boundary. Our focus is on a class of monotone finite difference approximations, which provide a strong form of…

Numerical Analysis · Mathematics 2023-06-05 Brittany Froese Hamfeldt , Axel G. R. Turnquist

An adaptive direct collocation method is developed for solving optimal control problems constrained by parabolic partial differential equations. The partial differential equation is first reformulated in a variational setting, where the…

Optimization and Control · Mathematics 2026-03-18 Alexander M. Davies , Sara Pollock , Miriam E. Dennis , Anil V. Rao

An initial-boundary value problem with a Caputo time derivative of fractional order $\alpha\in(0,1)$ is considered, solutions of which typically exhibit a singular behaviour at an initial time. An L2-type discrete fractional-derivative…

Numerical Analysis · Mathematics 2020-07-13 Natalia Kopteva

We study an expansion method for high-dimensional parabolic PDEs which constructs accurate approximate solutions by decomposition into solutions to lower-dimensional PDEs, and which is particularly effective if there are a low number of…

Analysis of PDEs · Mathematics 2016-11-08 Christoph Reisinger , Rasmus Wissmann

In this paper, we extend the uniform regularity estimates obtained by M. Avellanda and F. Lin in the paper of Compactness methods in the theory of homogenization (Comm. Pure Appl. Math. 40(1987), no.6, 803-847) to the more general second…

Analysis of PDEs · Mathematics 2015-12-08 Qiang Xu

In this paper we get error bounds for fully discrete approximations of infinite horizon problems via the dynamic programming approach. It is well known that considering a time discretization with a positive step size $h$ an error bound of…

Numerical Analysis · Mathematics 2026-02-09 Javier de Frutos , Julia Novo

We consider the following eigenvalue optimization in the composite membrane problem with fractional Laplacian: given a bounded domain $\Omega\subset \mathbb{R}^n$, $\alpha>0$ and $0<A<|\Omega|$, find a subset $D\subset \Omega$ of area $A$…

Analysis of PDEs · Mathematics 2020-09-23 María del Mar González , Ki-Ahm Lee , Taehun Lee

We propose \textit{DeepMartingale}, a deep-learning framework for the dual formulation of discrete-monitoring optimal stopping problems under continuous-time models. Leveraging a martingale representation, our method implements a…

Optimization and Control · Mathematics 2026-02-27 Junyan Ye , Hoi Ying Wong

For a generic discrete-time algorithm (DTA): $z^+=g(z,s)$, where $s$ is the step size, Lu (Math. Program., 194(1):1061--1112, 2022) proposed an $O(s^r)$-resolution ordinary differential equation (ODE) framework based on the backward error…

Optimization and Control · Mathematics 2026-03-10 Lixia Wang , Hao Luo

We prove a new generalization bound that shows for any class of linear predictors in Gaussian space, the Rademacher complexity of the class and the training error under any continuous loss $\ell$ can control the test error under all Moreau…

Machine Learning · Statistics 2022-10-24 Lijia Zhou , Frederic Koehler , Pragya Sur , Danica J. Sutherland , Nathan Srebro

We consider the problem of optimality, in a minimax sense, and adaptivity to the margin and to regularity in binary classification. We prove an oracle inequality, under the margin assumption (low noise condition), satisfied by an…

Statistics Theory · Mathematics 2016-08-16 Guillaume Lecué

We study the problem of minimizing a sum of local objective convex functions over a network of processors/agents. This problem naturally calls for distributed optimization algorithms, in which the agents cooperatively solve the problem…

Optimization and Control · Mathematics 2019-04-01 Fatemeh Mansoori , Ermin Wei

We solve variationally certain equations of stellar dynamics of the form $-\sum_i\partial_{ii} u(x) =\frac{|u|^{p-2}u(x)}{{\rm dist} (x,{\mathcal A} )^s}$ in a domain $\Omega$ of $\rn$, where ${\mathcal A} $ is a proper linear subspace of…

Analysis of PDEs · Mathematics 2007-05-23 Nassif Ghoussoub , Frederic Robert

We construct numerical integrators for Hamiltonian problems that may advantageously replace the standard Verlet time-stepper within Hybrid Monte Carlo and related simulations. Past attempts have often aimed at boosting the order of accuracy…

Numerical Analysis · Mathematics 2015-04-10 Sergio Blanes , Fernando Casas , J. M. Sanz-Serna

We study and characterize the optimal rates of convergence in periodic homogenization of linear elliptic equations in non-divergence form. We obtain that the optimal rate of convergence is either $O(\varepsilon)$ or $O(\varepsilon^2)$…

Analysis of PDEs · Mathematics 2022-01-07 Xiaoqin Guo , Hung V. Tran , Yifeng Yu

In this paper, we introduce a modification of the free boundary problem related to optimal stopping problems for diffusion processes. This modification allows the application of this PDE method in cases where the usual regularity…

Probability · Mathematics 2008-12-18 Ludger Rüschendorf , Mikhail A. Urusov

We study the error exponents in quantum hypothesis testing between two sets of quantum states, extending the analysis beyond the independent and identically distributed case to encompass composite correlated hypotheses. In particular, we…

Quantum Physics · Physics 2025-11-11 Kun Fang , Masahito Hayashi
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