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An exact formula for the expected length of the minimum spanning tree of a connected graph, with independent and identical edge distribution, is given, which generalizes Steele's formula in the uniform case. For a complete graph, the difference of expected lengths between exponential distribution, with rate one, and uniform distribution on the interval (0, 1) is shown to be positive and of rate ζ(3)/n. For wheel graphs, precise values of expected lengths are given via calculations of the associated Tutte polynomials.
A d-simplex is a collection of d + 1 sets such that every d of them has non-empty intersection and the intersection of all of them is empty. Fix k ≥ d + 2 ≥ 3 and let be a family of k-element subsets of an n-element set that contains no d-simplex. We prove that if , then there is a vertex x of such that the number of sets in omitting x is o(nk−1) (here o(1)→ 0 and n → ∞). A similar result when n/k is bounded from above was recently proved in [10].
Our main result is actually stronger, and implies that if for any ϵ < 0 and n sufficiently large, then contains d + 2 sets A, A1, . . . ,Ad+1 such that the Ais form a d-simplex, and A contains an element of ∩j≠iAj for each i. This generalizes, in asymptotic form, a recent result of Vestraëte and the first author [18], who proved it for d = 1, ϵ = 0 and n ≥ 2k.
An instance of a size-n stable marriage problem involves n men and n women, each individually ranking all members of opposite sex in order of preference as a potential marriage partner. A complete matching, a set of n marriages, is called stable if no unmatched man and woman prefer each other to their partners in the matching. It is known that, for every instance of marriage partner preferences, there exists at least one stable matching, and that there are instances with exponentially many stable matchings. Our focus is on a random instance chosen uniformly from among all (n!)2n possible instances. The second author had proved that the expected number of stable marriages is of order nlnn, while its likely value is of order n1/2−o(1) at least. In this paper the second moment of that number is shown to be of order (nlnn)2. The combination of the two moment estimates implies that the fraction of problem instances with roughly cnlnn solutions is at least 0.84. Whether this fraction is asymptotic to 1 remains an open question.
A collection of permutation classes is exhibited whose growth rates form a perfect set, thereby refuting some conjectures of Balogh, Bollobás and Morris.
Using various results from extremal set theory (interpreted in the language of additive combinatorics), we prove an asymptotically sharp version of Freiman's theorem in : if is a set for which |A + A| ≤ K|A| then A is contained in a subspace of size ; except for the error, this is best possible. If in addition we assume that A is a downset, then we can also cover A by O(K46) translates of a coordinate subspace of size at most |A|, thereby verifying the so-called polynomial Freiman–Ruzsa conjecture in this case. A common theme in the arguments is the use of compression techniques. These have long been familiar in extremal set theory, but have been used only rarely in the additive combinatorics literature.
Let q be a power of a prime p, and let n, d, ℓ be integers such that 1 ≤ n, 1 ≤ ℓ < q. Consider the modulo q complete ℓ-wide family:
We describe a Gröbner basis of the vanishing ideal I() of the set of characteristic vectors of over fields of characteristic p. It turns out that this set of polynomials is a Gröbner basis for all term orderings ≺, for which the order of the variables is xn ≺ xn−1 ≺ ⋅⋅⋅ ≺ x1.
We compute the Hilbert function of I(), which yields formulae for the modulo p rank of certain inclusion matrices related to .
We apply our results to problems from extremal set theory. We prove a sharp upper bound of the cardinality of a modulo q ℓ-wide family, which shatters only small sets. This is closely related to a conjecture of Frankl [13] on certain ℓ-antichains. The formula of the Hilbert function also allows us to obtain an upper bound on the size of a set system with certain restricted intersections, generalizing a bound proposed by Babai and Frankl [6].
The paper generalizes and extends the results of [15], [16] and [17].
A celebrated theorem of Friedgut says that every function f : {0, 1}n → {0, 1} can be approximated by a function g : {0, 1}n → {0, 1} with , which depends only on eO(If / ε) variables, where If is the sum of the influences of the variables of f. Dinur and Friedgut later showed that this statement also holds if we replace the discrete domain {0, 1}n with the continuous domain [0, 1]n, under the extra assumption that f is increasing. They conjectured that the condition of monotonicity is unnecessary and can be removed.
We show that certain constant-depth decision trees provide counter-examples to the Dinur–Friedgut conjecture. This suggests a reformulation of the conjecture in which the function g : [0, 1]n → {0, 1}, instead of depending on a small number of variables, has a decision tree of small depth. In fact we prove this reformulation by showing that the depth of the decision tree of g can be bounded by eO(If / ε2).
Furthermore, we consider a second notion of the influence of a variable, and study the functions that have bounded total influence in this sense. We use a theorem of Bourgain to show that these functions have certain properties. We also study the relation between the two different notions of influence.
Let r ≥ 3 and (c/rr)r log n ≥ 1. If G is a graph of order n and its largest eigenvalue μ(G) satisfiesthen G contains a complete r-partite subgraph with r − 1 parts of size ⌊(c/rr)r log n⌋ and one part of size greater than n1−cr−1.
This result implies the Erdős–Stone–Bollobás theorem, the essential quantitative form of the Erdős–Stone theorem. Another easy consequence is that if F1, F2, . . . are r-chromatic graphs satisfying v(Fn) = o(log n), then
Let k3reg(n, d) be the minimum number of triangles in d-regular graphs with n vertices. We find the exact value of k3reg(n, d) for d between and n/2. In addition, we identify the structure of the extremal graphs.
It is shown that the class of left-to-right regular languages coincides withthe class of languages that are acceptedby monotone deterministic RL-automata,in this way establishing a close correspondence between a classicalparsing algorithm and a certain restricted type of analysis by reduction.
This appendix reviews the mathematical notions used in this book. However, most of these are only used in few places, and so the reader might want to only quickly review Sections A.1 and A.2, and come back to the other sections as needed. In particular, apart from probability, the first part of the book essentially requires only comfort with mathematical proofs and some very basic notions of discrete math.
The topics described in this appendix are covered in greater depth in many texts and online sources. Almost all of the mathematical background needed is covered in a good undergraduate “discrete math for computer science” course as currently taught at many computer science departments. Some good sources for this material are the lecture notes by Papadimitriou and Vazirani [PV06], and the book by Rosen [Ros06].
The mathematical tool we use most often is discrete probability. Alon and Spencer [AS00b] is a great resource in this area. Also, the books of Mitzenmacher and Upfal [MU05] and Motwani and Raghavan [MR95] cover probability from a more algorithmic perspective.
Although knowledge of algorithms is not strictly necessary for this book, it would be quite useful. It would be helpful to review either one of the two recent books by Dasgupta et al. [DPV06] and Kleinberg and Tardos [KT06] or the earlier text by Cormen et al. [CLRS01]. This book does not require prior knowledge of computability and automata theory, but some basic familiarity with that theory could be useful: See Sipser's book [Sip96] for an excellent introduction.
One might imagine that P ≠ NP, but SAT is tractable in the following sense: for every ℓ there is a very short program that runs in time ℓ2 and correctly treats all instances of size ℓ.
– Karp and Lipton, 1982
This chapter investigates a model of computation called the Boolean circuit, which is a generalization of Boolean formulas and a simplified model of the silicon chips used to make modern computers. It is a natural model for nonuniform computation, which crops up often in complexity theory (e.g., see Chapters 19 and 20). In contrast to the standard (or uniform) TM model where the same TM is used on all the infinitely many input sizes, a nonuniform model allows a different algorithm to be used for each input size. Thus Karp and Lipton's quote above refers to the possibility that there could be a small and efficient silicon chip that is tailor-made to solve every 3SAT problem on say, 100,000 variables. The existence of such chips is not ruled out even if P ≠ NP. As the reader might now have guessed, in this chapter we give evidence that such efficient chip solvers for 3SAT are unlikely to exist, at least as the number of variables in the 3CNF formula starts to get large.
Why should we fear, when chance rules everything, And foresight of the future there is none; 'Tis best to live at random, as one can.
– Sophocles, Oedipus Rex
We present here the motivation and a general description of a method dealing with a class of problems in mathematical physics. The method is, essentially, a statistical approach to the study of differential equations.
– N. Metropolis and S. Ulam, “The Monte Carlo Method,” 1949
We do not assume anything about the distribution of the instances of the problem to be solved. Instead we incorporate randomization into the algorithm itself … It may seem at first surprising that employing randomization leads to efficient algorithms. This claim is substantiated by two examples. The first has to do with finding the nearest pair in a set of n points in ℝk. The second example is an extremely efficient algorithm for determining whether a number is prime.
– Michael Rabin, 1976
So far, we used the Turing machine (as defined in Chapter 1) as our standard model of computation. But there is one aspect of reality this model does not seem to capture: the ability to make random choices during the computation. (Most programming languages provide a built-in random number generator for this.) Scientists and philosophers may still debate if true randomness exists in the world, but it definitely seems that when tossing a coin (or measuring the results of other physical experiments) we get an outcome that is sufficiently random and unpredictable for all practical purposes.