Arithmetic combinatorics

Abstract: Section 1: Extremal and Probabilistic Combinatorics.- Problems Related to Graph Indices in Trees.- The edit distance in graphs: methods, results and generalizations.- Repetitions in graphs and sequences.- On Some Extremal Problems for Cycles in Graphs.- A survey of Turan problems for expansions.- Survey on matching, packing and Hamilton cycle problems on hypergraphs.- Rainbow Hamilton cycles in random graphs and hypergraphs.- Further applications of the Container Method.- Independent transversals and hypergraph matchings - an elementary approach.- Giant components in random graphs.- Infinite random graphs and properties of metrics.- Nordhaus-Gaddum Problems for Colin de Verdiere Type Parameters, Variants of Tree-width, and Related Parameters.- Algebraic aspects of the normalized Laplacian.- Poset-free Families of Subsets.- Mathematics of causal sets.- Section 2: Additive and Analytic Combinatorics.- Lectures on Approximate groups and Hilbert's 5th Problem.- Character sums and arithmetic combinatorics.- On sum-product problem.- Ajtai-Szemeredi Theorems over quasirandom groups.- Section 3: Enumerative and Geometric Combinatorics.- Moments of orthogonal polynomials and combinatorics.- The combinatorics of knot invariants arising from the study of Macdonald polynomials.- Some algorithmic applications of partition functions in combinatorics.- Partition Analysis, Modular Functions, and Computer Algebra.- A survey of consecutive patterns in permutations.- Unimodality Problems in Ehrhart Theory.- Face enumeration on simplicial complexes.- Simplicial and Cellular Trees.- Parametric Polyhedra with at least k Lattice Points: Their Semigroup Structure and the k-Frobenius Problem.- Dynamical Algebraic Combinatorics and the Homomesy Phenomenon.

Abstract: A famous theorem of Szemeredi asserts that all subsets of the integers with positive upper density will contain arbitrarily long arithmetic progressions. There are many different proofs of this deep theorem, but they are all based on a fundamental dichotomy between structure and randomness, which in turn leads (roughly speaking) to a decomposition of any object into a structured (low-complexity) component and a random (discorrelated) component. Important examples of these types of decompositions include the Furstenberg structure theorem and the Szemeredi regularity lemma. One recent application of this dichotomy is the result of Green and Tao establishing that the prime numbers contain arbitrarily long arithmetic progressions (despite having density zero in the integers). The power of this dichotomy is evidenced by the fact that the Green�Tao theorem requires surprisingly little technology from analytic number theory, relying instead almost exclusively on manifestations of this dichotomy such as Szemeredi�s theorem. In this paper we survey various manifestations of this dichotomy in combinatorics, harmonic analysis, ergodic theory, and number theory. As we hope to emphasize here, the underlying themes in these arguments are remarkably similar even though the contexts are radically different.

Abstract: Arithmetic combinatorics is often concerned with the problem of controlling the possible range of behaviours of arbitrary finite sets in a group or ring with respect to arithmetic operations such as addition or multiplication. Similarly, combinatorial geometry is often concerned with the problem of controlling the possible range of behaviours of arbitrary finite collections of geometric objects such as points, lines, or circles with respect to geometric operations such as incidence or distance. Given the presence of arbitrary finite sets in these problems, the methods used to attack these problems have primarily been combinatorial in nature. In recent years, however, many outstanding problems in these directions have been solved by algebraic means (and more specifically, using tools from algebraic geometry and/or algebraic topology), giving rise to an emerging set of techniques which is now known as the polynomial method. Broadly speaking, the strategy is to capture (or at least partition) the arbitrary sets of objects (viewed as points in some configuration space) in the zero set of a polynomial whose degree (or other measure of complexity) is under control; for instance, the degree may be bounded by some function of the number of objects. One then uses tools from algebraic geometry to understand the structure of this zero set, and thence to control the original sets of objects. While various instances of the polynomial method have been known for decades (e.g. Stepanov’s method, the combinatorial Nullstellensatz, or Baker’s theorem), the general theory of this method is still in the process of maturing; in particular, the limitations of the polynomial method are not well understood, and there is still considerable scope to apply deeper results from algebraic geometry or algebraic topology to strengthen the method further. In this survey we present several of the known applications of these methods, focusing on the simplest cases to illustrate the techniques. We will assume as little prior knowledge of algebraic geometry as possible.