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This work is a Master thesis supervised by Prof. Dr. H.W. Lenstra. Lenstra and Silverberg showed that each reduced order has a universal grading, which can be viewed as the `largest possible grading'. We present an algorithm to compute the…

Commutative Algebra · Mathematics 2019-11-11 Daniël M. H. van Gent

Given a text $T$ of length $n$, we propose a deterministic online algorithm computing the sparse suffix array and the sparse longest common prefix array of $T$ in $O(c \sqrt{\lg n} + m \lg m \lg n \lg^* n)$ time with $O(m)$ words of space…

Data Structures and Algorithms · Computer Science 2018-03-26 Johannes Fischer , Tomohiro I , Dominik Köppl

A recent line of works, initiated by Russo and Xu, has shown that the generalization error of a learning algorithm can be upper bounded by information measures. In most of the relevant works, the convergence rate of the expected…

Information Theory · Computer Science 2022-05-16 Xuetong Wu , Jonathan H. Manton , Uwe Aickelin , Jingge Zhu

Proper continued fractions are generalized continued fractions with positive integer numerators $a_i$ and integer denominators with $b_i\geq a_i$. In this paper we study the strength of approximation of irrational numbers to their…

Dynamical Systems · Mathematics 2024-12-09 Niels Langeveld , David Ralston

We present a formula for trigonometric orthosymplectic $R$-matrices associated with any parity sequence, and establish their factorization into the ordered product of $q$-exponents parametrized by positive roots in the corresponding reduced…

Representation Theory · Mathematics 2026-05-18 Kyungtak Hong , Alexander Tsymbaliuk

We generalize the array orthogonality property for perfect autocorrelation sequences to $n$-dimensional arrays. The generalized array orthogonality property is used to derive a number of $n$-dimensional perfect array constructions.

Information Theory · Computer Science 2014-12-11 Sam Blake , Andrew Tirkel

In this paper, we present a unified analysis of matrix completion under general low-dimensional structural constraints induced by {\em any} norm regularization. We consider two estimators for the general problem of structured matrix…

Machine Learning · Statistics 2018-11-26 Suriya Gunasekar , Arindam Banerjee , Joydeep Ghosh

Given a finite set of real numbers $A$, the generalised golden ratio is the unique real number $\mathcal{G}(A) > 1$ for which we only have trivial unique expansions in smaller bases, and have non-trivial unique expansions in larger bases.…

Number Theory · Mathematics 2016-09-12 Simon Baker , Wolfgang Steiner

If w is a word in d>1 letters and G is a finite group, evaluation of w on a uniformly randomly chosen d-tuple in G gives a random variable with values in G, which may or may not be uniform. It is known that if G ranges over finite simple…

Group Theory · Mathematics 2020-09-23 Michael Larsen

We introduce a generalized Fourier ratio, the \(\ell^1/\ell^2\) norm ratio of coefficients in an \emph{arbitrary} orthonormal system, as a single, basis-invariant measure of \emph{effective dimension} that governs fundamental limits across…

Classical Analysis and ODEs · Mathematics 2026-01-26 Will Burstein , Alex Iosevich , Hari Sarang Nathan

To provide generalized solutions if a given problem admits no actual solution is an important task in mathematics and the natural sciences. It has a rich history dating back to the early 19th century when Carl Friedrich Gauss developed the…

Functional Analysis · Mathematics 2011-02-09 Heinz H. Bauschke , Xianfu Wang , Calvin J. S. Wylie

Modern decision-making scenarios often involve data that is both high-dimensional and rich in higher-order contextual information, where existing bandits algorithms fail to generate effective policies. In response, we propose in this paper…

Machine Learning · Computer Science 2025-01-24 Jiannan Li , Yiyang Yang , Yao Wang , Shaojie Tang

We design and mathematically analyze sampling-based algorithms for regularized loss minimization problems that are implementable in popular computational models for large data, in which the access to the data is restricted in some way. Our…

Machine Learning · Computer Science 2019-06-04 Ryan R. Curtin , Sungjin Im , Ben Moseley , Kirk Pruhs , Alireza Samadian

We introduce the problem of variable-length source resolvability, where a given target probability distribution is approximated by encoding a variable-length uniform random number, and the asymptotically minimum average length rate of the…

Information Theory · Computer Science 2017-01-31 Hideki Yagi , Te Sun Han

As is the case of many signals produced by complex systems, language presents a statistical structure that is balanced between order and disorder. Here we review and extend recent results from quantitative characterisations of the degree of…

Computation and Language · Computer Science 2015-03-05 Marcelo A Montemurro , Damián H Zanette

We consider the problem of achieving average consensus in the minimum number of linear iterations on a fixed, undirected graph. We are motivated by the task of deriving lower bounds for consensus protocols and by the so-called "definitive…

Optimization and Control · Mathematics 2013-08-30 Julien M. Hendrickx , Raphaël M. Jungers , Alexander Olshevsky , Guillaume Vankeerberghen

In the application of autoregressive models the order of the model is often estimated using either a sequence of likelihood ratio tests, a likelihood based information criterion, or a residual based test. The properties of such procedures…

Statistics Theory · Mathematics 2007-06-13 Bent Nielsen

In this paper, we consider sums of generalized polygonal numbers with repeats, generalizing Fermat's polygonal number theorem which was proven by Cauchy. In particular, we obtain the minimal number of generalized $m$-gonal numbers required…

A novel approach is introduced to a very widely occurring problem, providing a complete, explicit resolution of it: minimisation of a convex quadratic under a general quadratic, equality or inequality, constraint. Completeness comes via…

Optimization and Control · Mathematics 2017-07-21 Casper Albers , Frank Critchley , John Gower

High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n. Since it is usually impossible to obtain consistent procedures unless $p/n\rightarrow0$, a…

Statistics Theory · Mathematics 2013-03-13 Sahand N. Negahban , Pradeep Ravikumar , Martin J. Wainwright , Bin Yu