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We propose a numerical method to spline-interpolate discrete signals and then apply the integral transforms to the corresponding analytical spline functions. This represents a robust and computationally efficient technique for estimating…

Numerical Analysis · Mathematics 2023-04-27 Yupeng Zhang , Yueyang Shen , Rongqian Zhang , Yuyao Liu , Yunjie Guo , Daxuan Deng , Ivo D. Dinov

Coupled-mode theory (CMT) is a powerful tool for simulating near-harmonic systems. In telecommunications, variations of the theory have been used extensively to study waveguides, both analytically and through numerical modelling. Analogous…

Instrumentation and Methods for Astrophysics · Physics 2024-12-11 Jonathan Lin

A discrete rotation algorithm can be apprehended as a parametric application $f\_\alpha$ from $\ZZ[i]$ to $\ZZ[i]$, whose resulting permutation ``looks like'' the map induced by an Euclidean rotation. For this kind of algorithm, to be…

Discrete Mathematics · Computer Science 2007-05-23 Bertrand Nouvel , Eric Remila

Finite unit norm tight frames provide Parseval-like decompositions of vectors in terms of redundant components of equal weight. They are known to be exceptionally robust against additive noise and erasures, and as such, have great potential…

Functional Analysis · Mathematics 2010-09-29 Peter G. Casazza , Matthew Fickus , Dustin G. Mixon

Fixed-parameter algorithms, approximation algorithms and moderately exponential algorithms are three major approaches to algorithms design. While each of them being very active in its own, there is an increasing attention to the connection…

Computational Complexity · Computer Science 2012-11-29 Bruno Escoffier , EunJung Kim , Vangelis Th. Paschos

We propose explicit symplectic integrators of molecular dynamics (MD) algorithms for rigid-body molecules in the canonical and isothermal-isobaric ensembles. We also present a symplectic algorithm in the constant normal pressure and lateral…

Statistical Mechanics · Physics 2007-05-23 Hisashi Okumura , Satoru G. Itoh , Yuko Okamoto

We unify the discrete Fourier transform (DFT), discrete cosine transform (DCT), Walsh-Hadamard, Haar wavelet, Karhunen-Lo\`eve transform, and several others along with their continuous counterparts (Fourier transform, Fourier series,…

Signal Processing · Electrical Eng. & Systems 2026-05-19 Mitchell A. Thornton

Parity-time-symmetric ($\mathcal{PT}$-symmetric) optical waveguide couplers offer a great potential for future applications in integrated optics. Studies of nonlinear $\mathcal{PT}$-symmetric couplers present new possibilities for…

Optics · Physics 2023-07-19 Wiktor Walasik , Chicheng Ma , Natalia M. Litchinitser

This paper develops a computational framework for Multi-Period Martingale Optimal Transport (MMOT), addressing convergence rates, algorithmic efficiency, and financial calibration. Our contributions include: (1) Theoretical analysis: We…

Computational Finance · Quantitative Finance 2026-04-21 Sri Sairam Gautam B

The simulation of large nonlinear dynamical systems, including systems generated by discretization of hyperbolic partial differential equations, can be computationally demanding. Such systems are important in both fluid and kinetic…

Plasma Physics · Physics 2021-06-14 Alexander Engel , Graeme Smith , Scott E. Parker

Digital Transforms have important applications on subjects such as channel coding, cryptography and digital signal processing. In this paper, two Fourier Transforms are considered, the discrete time Fourier transform (DTFT) and the finite…

Structured light, light tailored in its internal degrees of freedom, has become topical in numerous quantum and classical information processing protocols. In this work, we harness the high dimensional nature of structured light modulated…

Snapshot matrices of hyperbolic equations have a slow singular value decay, resulting in inefficient reduced-order models. We develop on the idea of inducing a faster singular value decay by computing snapshots on a transformed spatial…

Numerical Analysis · Mathematics 2020-09-18 Neeraj Sarna , Jan Giesselmann , Peter Benner

This paper introduces Generalized Fourier transform (GFT) that is an extension or the generalization of the Fourier transform (FT). The Unilateral Laplace transform (LT) is observed to be the special case of GFT. GFT, as proposed in this…

Signal Processing · Electrical Eng. & Systems 2022-04-06 Pushpendra Singh , Anubha Gupta , Shiv Dutt Joshi

Given a time series vector, how can we efficiently compute a specified part of Fourier coefficients? Fast Fourier transform (FFT) is a widely used algorithm that computes the discrete Fourier transform in many machine learning applications.…

Machine Learning · Computer Science 2020-08-31 Yong-chan Park , Jun-Gi Jang , U Kang

It is demonstrated is this letter that linear multistep methods for integrating ordinary differential equations can be used to develop a family of fast forward scattering algorithms with higher orders of convergence. Excluding the cost of…

Computational Physics · Physics 2018-03-28 Vishal Vaibhav

Nonlinear monotone transformations are used extensively in normalizing flows to construct invertible triangular mappings from simple distributions to complex ones. In existing literature, monotonicity is usually enforced by restricting…

Machine Learning · Computer Science 2022-06-07 Difeng Cai , Yuliang Ji , Huan He , Qiang Ye , Yuanzhe Xi

Finite (or Discrete) Fourier Transforms (FFT) are essential tools in engineering disciplines based on signal transmission, which is the case in most of them. FFT are related with circulant matrices, which can be viewed as group matrices of…

Number Theory · Mathematics 2013-01-08 Kanemitsu Shigeru , Waldschmidt Michel

We explore the class of exponential integrators known as exponential time differencing (ETD) method in this letter to design low complexity nonlinear Fourier transform (NFT) algorithms that compute discrete approximations of the scattering…

Computational Physics · Physics 2019-08-27 Vishal Vaibhav

Canonical transformation plays a fundamental role in simplifying and solving classical Hamiltonian systems. We construct flexible and powerful canonical transformations as generative models using symplectic neural networks. The model…

Statistical Mechanics · Physics 2020-04-29 Shuo-Hui Li , Chen-Xiao Dong , Linfeng Zhang , Lei Wang