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Coprime integers

About: Coprime integers is a research topic. Over the lifetime, 3322 publications have been published within this topic receiving 47362 citations. The topic is also known as: relatively prime & coprime integers.


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Book
23 Jun 1995
TL;DR: This book presents Semigroup Theory, a treatment of systems theory concepts in finite dimensions with a focus on Hankel Operators and the Nehari Problem.
Abstract: 1 Introduction.- 1.1 Motivation.- 1.2 Systems theory concepts in finite dimensions.- 1.3 Aims of this book.- 2 Semigroup Theory.- 2.1 Strongly continuous semigroups.- 2.2 Contraction and dual semigroups.- 2.3 Riesz-spectral operators.- 2.4 Delay equations.- 2.5 Invariant subspaces.- 2.6 Exercises.- 2.7 Notes and references.- 3 The Cauchy Problem.- 3.1 The abstract Cauchy problem.- 3.2 Perturbations and composite systems.- 3.3 Boundary control systems.- 3.4 Exercises.- 3.5 Notes and references.- 4 Inputs and Outputs.- 4.1 Controllability and observability.- 4.2 Tests for approximate controllability and observability.- 4.3 Input-output maps.- 4.4 Exercises.- 4.5 Notes and references.- 5 Stability, Stabilizability, and Detectability.- 5.1 Exponential stability.- 5.2 Exponential stabilizability and detectability.- 5.3 Compensator design.- 5.4 Exercises.- 5.5 Notes and references.- 6 Linear Quadratic Optimal Control.- 6.1 The problem on a finite-time interval.- 6.2 The problem on the infinite-time interval.- 6.3 Exercises.- 6.4 Notes and references.- 7 Frequency-Domain Descriptions.- 7.1 The Callier-Desoer class of scalar transfer functions.- 7.2 The multivariable extension.- 7.3 State-space interpretations.- 7.4 Exercises.- 7.5 Notes and references.- 8 Hankel Operators and the Nehari Problem.- 8.1 Frequency-domain formulation.- 8.2 Hankel operators in the time domain.- 8.3The Nehari extension problem for state linear systems.- 8.4 Exercises.- 8.5 Notes and references.- 9 Robust Finite-Dimensional Controller Synthesis.- 9.1 Closed-loop stability and coprime factorizations.- 9.2 Robust stabilization of uncertain systems.- 9.3 Robust stabilization under additive uncertainty.- 9.4 Robust stabilization under normalized left-coprime-factor uncertainty.- 9.5 Robustness in the presence of small delays.- 9.6 Exercises.- 9.7 Notes and references.- A. Mathematical Background.- A.1 Complex analysis.- A.2 Normed linear spaces.- A.2.1 General theory.- A.2.2 Hilbert spaces.- A.3 Operators on normed linear spaces.- A.3.1 General theory.- A.3.2 Operators on Hilbert spaces.- A.4 Spectral theory.- A.4.1 General spectral theory.- A.4.2 Spectral theory for compact normal operators.- A.5 Integration and differentiation theory.- A.5.1 Integration theory.- A.5.2 Differentiation theory.- A.6 Frequency-domain spaces.- A.6.1 Laplace and Fourier transforms.- A.6.2 Frequency-domain spaces.- A.6.3 The Hardy spaces.- A.7 Algebraic concepts.- A.7.1 General definitions.- A.7.2 Coprime factorizations over principal ideal domains.- A.7.3 Coprime factorizations over commutative integral domains.- References.- Notation.

2,923 citations

Journal ArticleDOI
TL;DR: A method for multiplying two integers modulo N while avoiding division by N, a representation of residue classes so as to speed modular multiplication without affecting the modular addition and subtraction algorithms.
Abstract: Let N > 1. We present a method for multiplying two integers (called N-residues) modulo N while avoiding division by N. N-residues are represented in a nonstandard way, so this method is useful only if several computations are done modulo one N. The addition and subtraction algorithms are unchanged. 1. Description. Some algorithms (1), (2), (4), (5) require extensive modular arith- metic. We propose a representation of residue classes so as to speed modular multiplication without affecting the modular addition and subtraction algorithms. Other recent algorithms for modular arithmetic appear in (3), (6). Fix N > 1. Define an A'-residue to be a residue class modulo N. Select a radix R coprime to N (possibly the machine word size or a power thereof) such that R > N and such that computations modulo R are inexpensive to process. Let R~l and N' be integers satisfying 0 N then return t - N else return t ■ To validate REDC, observe mN = TN'N = -Tmod R, so t is an integer. Also, tR = Tmod N so t = TR'X mod N. Thirdly, 0 < T + mN < RN + RN, so 0 < t < 2N. If R and N are large, then T + mN may exceed the largest double-precision value. One can circumvent this by adjusting m so -R < m < 0. Given two numbers x and y between 0 and N - 1 inclusive, let z = REDC(xy). Then z = (xy)R~x mod N, so (xR-l)(yR~x) = zRx mod N. Also, 0 < z < N, so z is the product of x and y in this representation. Other algorithms for operating on N-residues in this representation can be derived from the algorithms normally used. The addition algorithm is unchanged, since xR~x + yR~x = zR~x mod N if and only if x + y = z mod N. Also unchanged are

2,647 citations

Journal ArticleDOI
TL;DR: In this paper, it was shown that coprime right factorizations exist for the input-to-state mapping of a continuous-time nonlinear system provided that the smooth feedback stabilization problem is solvable for this system.
Abstract: It is shown that coprime right factorizations exist for the input-to-state mapping of a continuous-time nonlinear system provided that the smooth feedback stabilization problem is solvable for this system. It follows that feedback linearizable systems admit such fabrications. In order to establish the result, a Lyapunov-theoretic definition is proposed for bounded-input-bounded-output stability. The notion of stability studied in the state-space nonlinear control literature is related to a notion of stability under bounded control perturbations analogous to those studied in operator-theoretic approaches to systems; in particular it is proved that smooth stabilization implies smooth input-to-state stabilization. >

2,504 citations

Book
01 Jan 1988
TL;DR: In this article, the stable factorization approach is introduced to the synthesis of feedback controllers for linear control systems, where the controller is designed as a matrix over a fraction field associated with a commutative ring with identity, denoted by R, which also has no divisors of zero.
Abstract: This book introduces the so-called "stable factorization approach" to the synthesis of feedback controllers for linear control systems The key to this approach is to view the multi-input, multi-output (MIMO) plant for which one wishes to design a controller as a matrix over the fraction field F associated with a commutative ring with identity, denoted by R, which also has no divisors of zero In this setting, the set of single-input, single-output (SISO) stable control systems is precisely the ring R, while the set of stable MIMO control systems is the set of matrices whose elements all belong to R The set of unstable, meaning not necessarily stable, control systems is then taken to be the field of fractions F associated with R in the SISO case, and the set of matrices with elements in F in the MIMO case The central notion introduced in the book is that, in most situations of practical interest, every matrix P whose elements belong to F can be "factored" as a "ratio" of two matrices N,D whose elements belong to R, in such a way that N,D are coprime In the familiar case where the ring R corresponds to the set of bounded-input, bounded-output (BIBO)-stable rational transfer functions, coprimeness is equivalent to two functions not having any common zeros in the closed right half-plane including infinity However, the notion of coprimeness extends readily to discrete-time systems, distributed-parameter systems in both the continuous- as well as discrete-time domains, and to multi-dimensional systems Thus the stable factorization approach enables one to capture all these situations within a common framework The key result in the stable factorization approach is the parametrization of all controllers that stabilize a given plant It is shown that the set of all stabilizing controllers can be parametrized by a single parameter R, whose elements all belong to R Moreover, every transfer matrix in the closed-loop system is an affine function of the design parameter R Thus problems of reliable stabilization, disturbance rejection, robust stabilization etc can all be formulated in terms of choosing an appropriate R This is a reprint of the book Control System Synthesis: A Factorization Approach originally published by MIT Press in 1985 Table of Contents: Introduction / Proper Stable Rational Functions / Scalar Systems: An Introduction / Matrix Rings / Stabilization

1,840 citations

Journal ArticleDOI
TL;DR: The objects of ergodic theory -measure spaces with measure-preserving transformation groups- will be called processes, those of topological dynamics-compact metric spaces with groups of homeomorphisms-will be called flows, and what may be termed the "arithmetic" of these classes of objects is concerned.
Abstract: 0. Summary. The objects of ergodic theory -measure spaces with measure-preserving transformation groups-wil l be called processes, those of topological dynamics-compact metric spaces with groups of homeomorphisms-will be called flows. We shall be concerned with what may be termed the "arithmetic" of these classes of objects. One may form products of processes and of flows, and one may also speak of factor processes and factor flows. By analogy with the integers, we may say that two processes are relatively prime if they have no non-trivial factors in common. An alternative condition is that whenever the two processes appear as factors of a third process, then their product too appears as a factor. In our theories it is unknown whether these two conditions are equivalent. We choose the second of these conditions as the more useful and refer to it as disjointness. Our first applications of the concept of disjointness are to the classification of processes and flows. It will appear that certain classes of processes (flows) may be characterized by the property of being disjoint from the members of other classes of processes (flows). For example the processes with entropy 0 are just those which are disjoint from all Bernoulli flows. Another application of disjointness of processes is to the following filtering problem. If {xn} and {Yn} represent two stationary stochastic processes, when can {xn} be filtered perfectly from {Xn + Yn}? We will find (Part I, §9) that a sufficient condition is the disjointness of the processes in question. For flows the principal application of disjointness is to the ~tudy of properties of minimal sets (Part III). Consider the flow on the unit circle K = {z: [zl = 1 } that arises from the transformation z --~ z 2. What can be said about the "size" of the minimal sets for this flow, that is, closed subsets of K invariant under z ~ z ~, but not containing proper subsets with these properties. Uncountably many such minimal sets exist in K. Writing z = exp (2~ri Ean/2n), an = 0, 1, we see that this amounts to studying the mini-

952 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202389
2022231
2021242
2020240
2019206
2018173