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New algorithms are presented for computing annihilating polynomials of Toeplitz, Hankel, and more generally Toeplitz+ Hankel-like matrices over a field. Our approach follows works on Coppersmith's block Wiedemann method with structured…

Symbolic Computation · Computer Science 2021-04-07 Clément Pernet , Hippolyte Signargout , Pierre Karpman , Gilles Villard

We compute the nonlinearity of Boolean functions with Groebner basis techniques, providing two algorithms: one over the binary field and the other over the rationals. We also estimate their complexity. Then we show how to improve our…

Information Theory · Computer Science 2014-04-11 E. Bellini , I. Simonetti , M. Sala

A fast simple O(\log n) iteration algorithm for individual Lucas numbers is given. This is faster than using Fibonacci based methods because of the structure of Lucas numbers. Using a sqrt 5 conversion factor on Lucus numbers gives a faster…

Discrete Mathematics · Computer Science 2010-12-02 L. F. Johnson

We describe an algorithm for fast multiplication of skew polynomials. It is based on fast modular multiplication of such skew polynomials, for which we give an algorithm relying on evaluation and interpolation on normal bases. Our…

Symbolic Computation · Computer Science 2017-02-07 Xavier Caruso , Jérémy Le Borgne

This paper deals with simultaneously fast and in-place algorithms for formulae where the result has to be linearly accumulated: some output variables are also input variables, linked by a linear dependency. Fundamental examples include the…

Symbolic Computation · Computer Science 2025-11-07 Jean-Guillaume Dumas , Bruno Grenet

We develop the first fast spectral algorithm to decompose a random third-order tensor over $\mathbb{R}^d$ of rank up to $O(d^{3/2}/\text{polylog}(d))$. Our algorithm only involves simple linear algebra operations and can recover all…

Machine Learning · Computer Science 2022-06-30 Jingqiu Ding , Tommaso d'Orsi , Chih-Hung Liu , Stefan Tiegel , David Steurer

We introduce and investigate the asymptotic behaviour of the trajectories of a second order dynamical system with Tikhonov regularization for solving a monotone equation with single valued, monotone and continuous operator acting on a real…

Optimization and Control · Mathematics 2024-11-27 Ernö Robert Csetnek , Szilárd Csaba László

A well known numerical task is the inversion of large symmetric tridiagonal Toeplitz matrices, i.e., matrices whose entries equal $a$ on the diagonal and $b$ on the extra diagonals ($a, b\in \mathbb R$). The inverses of such matrices are…

Numerical Analysis · Mathematics 2016-11-29 Manuel Radons

Topology Optimization (TO), which maximizes structural robustness under material weight constraints, is becoming an essential step for the automatic design of mechanical parts. However, existing TO algorithms use the Finite Element Analysis…

Robotics · Computer Science 2022-04-14 Zherong Pan , Xifeng Gao , Kui Wu

We present an accelerated, or 'look-ahead' version of the Newton-Dinkelbach method, a well-known technique for solving fractional and parametric optimization problems. This acceleration halves the Bregman divergence between the current…

Data Structures and Algorithms · Computer Science 2021-05-24 Daniel Dadush , Zhuan Khye Koh , Bento Natura , László A. Végh

We develop a fast divided-and-conquer indirect collocation method for the homogeneous Dirichlet boundary value problem of variable-order space-fractional diffusion equations. Due to the impact of the space-dependent variable order, the…

Numerical Analysis · Mathematics 2019-07-09 Jinhong Jia , Xiangcheng Zheng , Hong Wang

The Tikhonov-Phillips method is widely used for regularizing ill-posed inverse problems mainly due to the simplicity of its formulation as an optimization problem. The use of different penalizers in the functionals associated to the…

Functional Analysis · Mathematics 2011-08-23 Gisela L. Mazzieri , Ruben D. Spies , Karina G. Temperini

In this work, we propose a high-order regularization method to solve the ill-conditioned problems in robot localization. Numerical solutions to robot localization problems are often unstable when the problems are ill-conditioned. A typical…

Robotics · Computer Science 2025-05-07 Xinghua Liu , Ming Cao

It is common to have to process signals, whose values are points on the 3-D sphere. We consider a Tikhonov-type regularization model to smoothen or interpolate sphere-valued signals defined on arbitrary graphs. We propose a convex…

Optimization and Control · Mathematics 2022-07-26 Laurent Condat

In 1989 we proposed to employ Vandermonde and Hankel multipliers to transform into each other the matrix structures of Toeplitz, Hankel, Vandermonde and Cauchy types as a means of extending any successful algorithm for the inversion of…

Numerical Analysis · Mathematics 2013-03-05 Victor Y. Pan

Topology Optimization (TO) provides a systematic approach for obtaining structure design with optimum performance of interest. However, the process requires numerical evaluation of objective function and constraints at each iteration, which…

Machine Learning · Computer Science 2022-03-22 Ren Kai Tan , Chao Qian , Dan Xu , Wenjing Ye

The inverse-free extreme learning machine (ELM) algorithm proposed in [4] was based on an inverse-free algorithm to compute the regularized pseudo-inverse, which was deduced from an inverse-free recursive algorithm to update the inverse of…

Machine Learning · Computer Science 2020-04-29 Hufei Zhu

In this article, we investigate both forward and backward problems for coupled systems of time-fractional diffusion equations, encompassing scenarios of strong coupling. For the forward problem, we establish the well-posedness of the…

Analysis of PDEs · Mathematics 2025-08-19 Dian Feng , Yikan Liu , Shuai Lu

We show that every n-by-n matrix is generically a product of [n/2] + 1 Toeplitz matrices and always a product of at most 2n+5 Toeplitz matrices. The same result holds true if the word "Toeplitz" is replaced by "Hankel", and the generic…

Algebraic Geometry · Mathematics 2014-07-04 Ke Ye , Lek-Heng Lim

Optimization, a key tool in machine learning and statistics, relies on regularization to reduce overfitting. Traditional regularization methods control a norm of the solution to ensure its smoothness. Recently, topological methods have…

Machine Learning · Computer Science 2020-11-11 Arnur Nigmetov , Aditi S. Krishnapriyan , Nicole Sanderson , Dmitriy Morozov