Error bounds for splitting methods in unitary problems
- URL: http://arxiv.org/abs/2604.01026v1
- Date: Wed, 01 Apr 2026 15:33:05 GMT
- Title: Error bounds for splitting methods in unitary problems
- Abstract summary: We present a systematic analysis of both local and global errors arising from arbitrary splitting methods applied to unitary problems.<n>The first is expressed in terms of operator norms, while the second is formulated using norms of commutators and can, under suitable assumptions, be extended to certain classes of operators.
- Score: 45.88028371034407
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Splitting methods constitute a widely used class of numerical integrators for ordinary and partial differential equations, particularly well suited to problems that can be decomposed into simpler subproblems. High-order splitting schemes are available that achieve high accuracy while preserving key qualitative properties of the underlying dynamical system, and are successfully used across a broad range of fields. In this work, we present a systematic analysis of both local and global errors arising from arbitrary splitting methods applied to unitary problems. Two complementary types of error estimates are derived. The first is expressed in terms of operator norms, while the second is formulated using norms of commutators and can, under suitable assumptions, be extended to certain classes of unbounded operators. Special attention is devoted to the case where only two operators are involved. The theoretical results are illustrated by deriving explicit error bounds for some representative schemes.
Related papers
- Generalization Guarantees for Multi-Input Neural Operator Learning in Sobolev Spaces [15.712274378790424]
We develop approximation and generalization error estimates for multi-input neural operators, with the output error measured in Sobolev norms.<n>In particular, in the balanced regime, the approximation and generalization rates are governed by the interaction between the input dimensions, regularities, and Sobolev orders.
arXiv Detail & Related papers (2026-06-16T02:00:59Z) - Primal Methods for Variational Inequality Problems with Functional Constraints [25.261426717550293]
We propose a primal method, termed Constrained Gradient Method (CGM), for addressing functional constrained variational inequality problems.<n>Our algorithms match the complexity of projection-based methods in terms of operator queries for both monotone and strongly monotone settings.
arXiv Detail & Related papers (2024-03-19T16:03:03Z) - Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions [18.086061048484616]
We study finite-sum monotone inclusion problems, which model broad classes of equilibrium problems.
Our main contributions are variants of the classical Halpern iteration that employ variance reduction to obtain improved complexity guarantees.
We argue that, up to poly-logarithmic factors, this complexity is unimprovable in the monotone Lipschitz setting.
arXiv Detail & Related papers (2023-10-04T17:24:45Z) - A Hybrid System for Systematic Generalization in Simple Arithmetic
Problems [70.91780996370326]
We propose a hybrid system capable of solving arithmetic problems that require compositional and systematic reasoning over sequences of symbols.
We show that the proposed system can accurately solve nested arithmetical expressions even when trained only on a subset including the simplest cases.
arXiv Detail & Related papers (2023-06-29T18:35:41Z) - Conditional expectation using compactification operators [0.0]
This paper describes an operator theoretic approach to estimating the conditional expectation.
Kernel integral operators are used as a compactification tool, to set up the estimation problem as a linear inverse problem in a reproducing kernel Hilbert space.
arXiv Detail & Related papers (2023-06-18T16:11:40Z) - Constraining Gaussian Processes to Systems of Linear Ordinary
Differential Equations [5.33024001730262]
LODE-GPs follow a system of linear homogeneous ODEs with constant coefficients.
We show the effectiveness of LODE-GPs in a number of experiments.
arXiv Detail & Related papers (2022-08-26T09:16:53Z) - Optimal variance-reduced stochastic approximation in Banach spaces [114.8734960258221]
We study the problem of estimating the fixed point of a contractive operator defined on a separable Banach space.
We establish non-asymptotic bounds for both the operator defect and the estimation error.
arXiv Detail & Related papers (2022-01-21T02:46:57Z) - Predicting Unreliable Predictions by Shattering a Neural Network [145.3823991041987]
Piecewise linear neural networks can be split into subfunctions.
Subfunctions have their own activation pattern, domain, and empirical error.
Empirical error for the full network can be written as an expectation over subfunctions.
arXiv Detail & Related papers (2021-06-15T18:34:41Z) - Dimension Free Generalization Bounds for Non Linear Metric Learning [61.193693608166114]
We provide uniform generalization bounds for two regimes -- the sparse regime, and a non-sparse regime.
We show that by relying on a different, new property of the solutions, it is still possible to provide dimension free generalization guarantees.
arXiv Detail & Related papers (2021-02-07T14:47:00Z) - Optimal oracle inequalities for solving projected fixed-point equations [53.31620399640334]
We study methods that use a collection of random observations to compute approximate solutions by searching over a known low-dimensional subspace of the Hilbert space.
We show how our results precisely characterize the error of a class of temporal difference learning methods for the policy evaluation problem with linear function approximation.
arXiv Detail & Related papers (2020-12-09T20:19:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.