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Computational group theory (CGT) is a subfield of symbolic algebra; it deals with the design, analysis, and implementation of algorithms for manipulating groups. It is an interdisciplinary area between mathematics and computer science. The major areas of CGT are the algorithms for finitely presented groups, polycyclic and finite solvable groups, permutation groups, matrix groups, and representation theory.
The topic of this book is the third of these areas. Permutation groups are the oldest type of representations of groups; in fact, the work of Galois on permutation groups, which is generally considered as the start of group theory as a separate branch of mathematics, preceded the abstract definition of groups by about a half a century. Algorithmic questions permeated permutation group theory from its inception. Galois group computations, and the related problem of determining all transitive permutation groups of a given degree, are still active areas of research (see [Hulpke, 1996]). Mathieu's constructions of his simple groups also involved serious computations.
Nowadays, permutation group algorithms are among the best developed parts of CGT, and we can handle groups of degree in the hundreds of thousands. The basic ideas for handling permutation groups appeared in [Sims, 1970, 1971a]; even today, Sims's methods are at the heart of most of the algorithms.
Exploiting special properties of solvable groups, [Sims, 1990] describes a method for constructing a strong generating set. Recall that a finite group G is solvable if and only if it is polycyclic, that is, there exists a sequence of elements (y1, …, yr) such that G = 〈y1, …, yr〉 and for all i ∊ [1, r– 1], yi normalizes
The main idea is that given an SGS for a group H ≤ Sym(Ω) and y ∊ Sym(Ω) such that y normalizes H, an SGS for 〈H, y〉 can be constructed without sifting of Schreier generators. The method is based on the following observation.
Lemma 7.1.1.Suppose that G = 〈H, y〉 ≤ Sym(Ω) and y normalizes H. For a fixed Then
m is an integer and there exists h ∊ H such that z := ymh fixes α
z normalizes Hα; and
Gα, z〉.
Proof. (i) By Lemma 6.1.7, Δ is the disjoint union of G-images of Γ. Moreover, the G-images of Γ are cyclically permuted by y and m is the smallest integer such that Γym = Γ. In particular, αym Ω Γ, so there exists h Ω H with the desired property.
A celebrated theorem of Turán asserts that every graph on n vertices with more than $\frac{r\,{-}\,1}{2r}n^2$ edges contains a copy of a complete graph $K_r+1$. In this paper we consider the following more general question. Let G be a $K_r+1-free graph of order n and let α be a constant, 0<α≤1. How dense can every induced subgraph of G on αn vertices be? We prove the following local density extension of Turán's theorem.
For every integer $r\geq 2$ there exists a constant $c_r < 1$ such that, if $c_r < \alpha < 1$ and every αn vertices of G span more than
edges, then G contains a copy of $K_r+1$. This result is clearly best possible and answers a question of Erdős, Faudree, Rousseau and Schelp [5].
In addition, we prove that the only $K_r+1-free graph of order n, in which every αn vertices span at least $\frac{r\,{-}\,1}{2r}(2\alpha -1)n^2$ edges, is a Turán graph. We also obtain the local density version of the Erdős–Stone theorem.
In this paper we study random linear systems with $k > 3$ variables per equation over the finite field GF(2), or equivalently k-XOR-CNF formulas. In a previous paper Creignou and Daudé proved that there exists a phase transition exhibiting a sharp threshold, for the consistency (satisfiability) of such systems (formulas). The control parameter for this transition is the ratio of the number of equations to the number of variables, and the scale for which the transition occurs remains somewhat elusive. In this paper we establish, for any $k > 3$, non-trivial lower and upper estimates of the value of the control ratio for which the phase transition occurs. For $k=3$ we get 0.89 and 0.93, respectively. Moreover, we give experimental results for $k=3$ suggesting that the critical ratio is about 0.92. Our estimates are clearly close to the critical ratio.
In this chapter, we start the main topic of this book with an overview of permutation group algorithms.
Polynomial-Time Algorithms
In theoretical computer science, a universally accepted measure of efficiency is polynomial-time computation. In the case of permutation group algorithms, groups are input by a list of generators. Given G = 〈S〉 ≤ Sn, the input is of length |S|n and a polynomial-time algorithm should run in O((|S|n)c) for some fixed constant c. In practice, |S| is usually small: Many interesting groups, including all finite simple groups, can be generated by two elements, and it is rare that in a practical computation a permutation group is given by more than ten generators. On the theoretical side, any G ≤ Sn can be generated by at most n/2 permutations (cf. [McIver and Neumann, 1987]). Moreover, any generating set S can be easily reduced to less than n2 generators in O(|S|n2) time by a deterministic algorithm (cf. Exercise 4.1), and in Theorem 10.1.3 we shall describe how to construct at most n – 1 generators for any G ≤ Sn. Hence, we require that the running time of a polynomial-time algorithm is O(nc + |S|n2) for some constant c.
In this book, we promote a slightly different measure of complexity involving n, |S|, and log |G| (cf. Section 3.2), which better reflects the practical performance of permutation group algorithms.
We provide an alternative, simpler and more general derivation of the Clarkson–Shor probabilistic technique [7] and use it to obtain several extensions and new combinatorial bounds.
In this chapter, we collect some basic algorithms that serve as building blocks to higher level problems. Frequently, the efficient implementation of these low-level algorithms is the key to the practicality of the more glamorous, high-level problems that use them as subroutines.
Consequences of the Schreier–Sims Method
The major applications of the Schreier–Sims SGS construction are membership testing in groups and finding the order of groups, but an additional benefit of the algorithm is that its methodology can be applied to solve a host of other problems in basic permutation group manipulation. We list the most important applications in this section. The running times depend on which version of the Schreier–Sims algorithm we use; in particular, all tasks listed in this section can be performed by nearly linear-time Monte Carlo algorithms. For use in Chapter 6, we also point out that if we already have a nonredundant base and SGS for the input group then the algorithms presented in Sections 5.1.1–5.1.3 are all Las Vegas. Concerning the closure algorithms in Section 5.1.4, see Exercise 5.1.
Pointwise Stabilizers
Any version of the Schreier–Sims method presented in Chapter 4 can be easily modified to yield the pointwise stabilizer of some subset of the permutation domain. Suppose that G = 〈S〉 ≤ Sym(Ω) and Δ ⊆ Ω are given, and we need generators for G(Δ).
The results of this paper concern the ‘large spectra’ of sets, by which we mean the set of points in ${\bb F}_p^{\times}$ at which the Fourier transform of a characteristic function $\chi_A$, $A\subseteq {\bb F}_p$, can be large. We show that a recent result of Chang concerning the structure of the large spectrum is best possible. Chang's result has already found a number of applications in combinatorial number theory.
We also show that if $|A|=\lfloor {p/2}\rfloor$, and if $R$ is the set of points $r$ for which $|\hat{\chi}_A(r)|\geqslant \alpha p$, then almost nothing can be said about $R$ other than that $|R|\ll \alpha^{-2}$, a trivial consequence of Parseval's theorem.
For an integer $s\ges 2$, a property $\P^{(s)}$ is an infinite class of s-uniform hypergraphs closed under isomorphism. We say that a property $\P^{(s)}$ is \emph{hereditary\/} if~$\P^{(s)}$ is closed under taking induced subhypergraphs. Thus, for some `forbidden class' $\FF=\{\F_i^{(s)}\:i\in I\}$ of s-uniform hypergraphs, $\P^{(s)}$ is the set of all s-uniform hypergraphs not containing any $\F_i^{(s)}\in\FF$ as an induced subhypergraph. Let $\P^{(s)}_n$ be those hypergraphs of $\P^{(s)}$ on some fixed n-vertex set. For a set of s-uniform hypergraphs $\FF=\{\F_i^{(s)}\:i\in I\}$, let
where the maximum is taken over all $\M$ and $\N\subseteq[n]^s$ with $\M\cap\N=\emptyset$ such that, for all $\G\subseteq[n]^s{\setminus}(\M\cup\N)$, no $\F_i^{(s)}\in\FF$ appears as an induced subhypergraph of $\G\cup\M$. We show that
holds for $s=3$ and any hereditary property $\P^{(3)}$, where $\FF$ is a forbidden class associated with $\P^{(3)}$. This result complements a collection of analogous theorems already proved for graphs (i.e., $s=2$).
Constructor-Based Conditional Rewriting Logic is a general framework for integrating first-order functional and logic programming which gives an algebraic semantics for nondeterministic functional-logic programs. In the context of this formalism, we introduce a simple notion of program module as an open program which can be extended together with several mechanisms to combine them. These mechanisms are based on a reduced set of operations. However, the high expressiveness of these operations enable us to model typical constructs for program modularization like hiding, export/import, genericity/instantiation, and inheritance in a simple way. We also deal with the semantic aspects of the proposal by introducing an immediate consequence operator, and studying several alternative semantics for a program module, based on this operator, in the line of logic programming: the operator itself, its least fixpoint (the least model of the module), the set of its pre-fixpoints (term models of the module), and some other variations in order to find a compositional and fully abstract semantics w.r.t. the set of operations and a natural notion of observability.
We introduce a methodology and framework for expressing general preference information in logic programming under the answer set semantics. An ordered logic program is an extended logic program in which rules are named by unique terms, and in which preferences among rules are given by a set of atoms of form s [pr] t where s and t are names. An ordered logic program is transformed into a second, regular, extended logic program wherein the preferences are respected, in that the answer sets obtained in the transformed program correspond with the preferred answer sets of the original program. Our approach allows the specification of dynamic orderings, in which preferences can appear arbitrarily within a program. Static orderings (in which preferences are external to a logic program) are a trivial restriction of the general dynamic case. First, we develop a specific approach to reasoning with preferences, wherein the preference ordering specifies the order in which rules are to be applied. We then demonstrate the wide range of applicability of our framework by showing how other approaches, among them that of Brewka and Eiter, can be captured within our framework. Since the result of each of these transformations is an extended logic program, we can make use of existing implementations, such as dlv and smodels. To this end, we have developed a publicly available compiler as a front-end for these programming systems.
To improve precision and efficiency, sharing analysis should track both freeness and linearity. The abstract unification algorithms for these combined domains are suboptimal, hence there is scope for improving precision. This paper proposes three optimisations for tracing sharing in combination with freeness and linearity. A novel connection between equations and sharing abstractions is used to establish correctness of these optimisations even in the presence of rational trees. A method for pruning intermediate sharing abstractions to improve efficiency is also proposed. The optimisations are lightweight and therefore some, if not all, of these optimisations will be of interest to the implementor.
Prioritized default reasoning has illustrated its rich expressiveness and flexibility in knowledge representation and reasoning. However, many important aspects of prioritized default reasoning have yet to be thoroughly explored. In this paper, we investigate two properties of prioritized logic programs in the context of answer set semantics. Specifically, we reveal a close relationship between mutual defeasibility and uniqueness of the answer set for a prioritized logic program. We then explore how the splitting technique for extended logic programs can be extended to prioritized logic programs. We prove splitting theorems that can be used to simplify the evaluation of a prioritized logic program under certain conditions.
G. Gierz, University of California, Riverside,K. H. Hofmann, Technische Universität, Darmstadt, Germany,K. Keimel, Technische Universität, Darmstadt, Germany,J. D. Lawson, Louisiana State University,M. Mislove, Tulane University, Louisiana,D. S. Scott, Carnegie Mellon University, Pennsylvania