Essential norms of weighted composition operators and Aleksandrov measures

We derive exact formulas for the essential and weak essential norms of weighted analytic composition operators acting on certain function spaces in the unit disc, extending and improving earlier results due to Sarason and to Kriete and Moorhouse. Differences of composition operators are also considered. The formulas involve the Aleksandrov measures associated to the symbol of the operator. The results are based on a variant of a general method due to Weis of constructing best compact and weakly compact approximants for linear operators on L¹ spaces.     

Subsets of Euclidean Space with Nearly Maximal Gowers Norms

A set E ⊂ ℝd whose indicator function 1E has maximal Gowers norm, among all sets of equal measure, is an ellipsoid up to Lebesgue null sets. If 1E has nearly maximal Gowers norm then E...     

On the structure of digital explicit nonlinear and inversive pseudorandom number generators

We analyze the lattice structure and distribution of the digital explicit inversive pseudorandom number generator introduced by Niederreiter and Winterhof as well as of a general digital explicit nonlinear generator. In particular, we extend a lattice test designed for this class of pseudorandom number generators to parts of the period and arbitrary lags and prove that these generators pass this test up to very high dimensions. We also analyze the behavior of digital explicit inversive and nonlinear generators under another very strong lattice test which in its easiest form can be traced back to Marsaglia and provides a complexity measure essentially equivalent to linear complexity.     

Distinction between several subsets of fuzzy measures

In this paper, we place several fuzzy measure subsets in relation one with the other. The subsets under study are those corresponding to the definitions of probability measure. Sugeno's g_λ-measure, Shafer's belief function and Zadeh's possibility measure. We study the intersection of these subsets and we show the particular role of Dirac's measures in this comparison. We limit ourself to the case of mappins whose domain is the collection of all subsets of a finite set.Finally, the obtained partial results are summarized in only one figure which shoul clarify the specificity of each of the above definitions.     

Products of polynomials in uniform norms

We study inequalities connecting a product of uniform norms of polynomials with the norm of their product. This subject includes the well known Gel'fond-Mahler inequalities for the unit disk and Kneser inequality for the segment . Using tools of complex analysis and potential theory, we prove a sharp inequality for norms of products of algebraic polynomials over an arbitrary compact set of positive logarithmic capacity in the complex plane. The above classical results are contained in our theorem as special cases.

It is shown that the asymptotically extremal sequences of polynomials, for which this inequality becomes an asymptotic equality, are characterized by their asymptotically uniform zero distributions. We also relate asymptotically extremal polynomials to the classical polynomials with asymptotically minimal norms.

     

On a family of pseudorandom binary sequences

Recently, numerous constructions have been given for finite pseudorandom binary sequences. However, in many applications, e.g., in cryptography one needs large families of good pseudorandom sequences. Very Recently L.~Goubin, C.~Mauduit, A.~Sárkzy succeeded in constructing large families of pseudorandom binary sequences based on the Legendre symbol. In this paper we will generate another type of large family of pseudorandom sequences by using the notion of index (discrete logarithm).     

On pseudorandom properties of multiplicative functions

In an earlier paper [3] Cassaigne et al studied the pseudorandom properties of the Liouville function. In this paper some of their results are generalized and sharpened considerably. This revised version was published online in June 2006 with corrections to the Cover Date.     

On a problem of D.H. Lehmer and pseudorandom binary sequences

Let p be an odd prime, and f(x), g(x) ∈ [x]. Define

Collision and avalanche effect in families of pseudorandom binary sequences

Recently a constructive theory of pseudorandomness of binary sequences has been developed and many constructions for binary sequences with strong pseudorandom properties have been given. In the applications one usually needs large families of binary sequences of this type. In this paper we adapt the notions of collision and avalanche effect to study these pseudorandom properties of families of binary sequences. We test two of the most important constructions for these pseudorandom properties, and it turns out that one of the two constructions is ideal from this point of view as well, while the other construction does not possess these pseudorandom properties. Communicated by Attila Pethő     

1‐Factorizations of pseudorandom graphs

A 1‐factorization of a graph G is a collection of edge‐disjoint perfect matchings whose union is E(G). In this paper, we prove that for any ?>0, an (n,d,λ)‐graph G admits a 1‐factorization provided that n is even, C₀ ≤ d ≤ n?1 (where C₀=C₀(?) is a constant depending only on ?), and λ ≤ d^1??. In particular, since (as is well known) a typical random d‐regular graph G_n,d is such a graph, we obtain the existence of a 1‐factorization in a typical G_n,d for all C₀ ≤ d ≤ n?1, thereby extending to all possible values of d results obtained by Janson, and independently by Molloy, Robalewska, Robinson, and Wormald for fixed d. Moreover, we also obtain a lower bound for the number of distinct 1‐factorizations of such graphs G, which is better by a factor of 2^nd/2 than the previously best known lower bounds, even in the simplest case where G is the complete graph.     

Concatenation of pseudorandom binary sequences

In the applications it may occur that our initial pseudorandom binary sequence turns out to be not long enough, thus we have to take the concatenation or merging of it with other pseudorandom binary sequences. Here our goal is study when we can form the concatenation of several pseudorandom binary sequences belonging to a given family? We introduce and study new measures which can be used for answering this question.     

Composite norms and perfect conditioning

We study condition numbers for composite norms and analyze the situations where perfect conditioning occurs.     

Construction of pseudorandom binary sequences using additive characters over GF(2 k )

In a series of papers Mauduit and Sárközy (partly with coauthors) studied finite pseudorandom binary sequences and they constructed sequences with strong pseudorandom properties. In these constructions fields with prime order were used. In this paper a new construction is presented, which is based on finite fields of order 2 ^k .     

Inversive congruential pseudorandom numbers: distribution of triples

This paper deals with the inversive congruential method with power of two modulus for generating uniform pseudorandom numbers. Statistical independence properties of the generated sequences are studied based on the distribution of triples of successive pseudorandom numbers. It is shown that, on the average over the parameters in the inversive congruential method, the discrepancy of the corresponding point sets in the unit cube is of an order of magnitude between and . The method of proof relies on a detailed discussion of the properties of certain exponential sums.

     

Rough norms in spaces of operators

We investigate sufficient and necessary conditions for the space of bounded linear operators between two Banach spaces to be rough or average rough. Our main result is that is δ‐average rough whenever is δ‐average rough and Y is alternatively octahedral. This allows us to give a unified improvement of two theorems by Becerra Guerrero, López‐Pérez, and Rueda Zoca [J. Math. Anal. Appl. 427 (2015)].     

Perfect graphs and norms

For a given undirected graphG=(V, ) withn vertices we define four norms on ⁿ , namely, where (resp.) stands for the family of all maximal cliques inG (resp. , the complement ofG). The goal of this note is to demonstrate the usefulness of some notions and techniques from functional analysis in graph theory by showing how Theorem 2.1 (G is -perfect if and only if the norms are equal) together with the reflexivity of the space ⁿ equipped with either of the norms above easily yield one new result (Theorem 2.2) and two known characterizations of perfect graphs (Theorems 2.3–2.4). Namely, Theorem 2.2 provides a characterization of -perfection that is strongly related to that of Lovász (1972). It implies that the Lovász inequality is exactly the classical Schwartz inequality for the space ( ⁿ , ·) restricted to (0, 1) vectorsx, y satisfyingx = y. Theorem 2.3 is well known as the Perfect Graph Theorem, while Theorem 2.4, due to V. Chvátal and D.R. Fulkerson, characterizes -perfection of a graphG in terms of the equality between the vertex packing polytope ofG and the fractional vertex packing polytope ofG.     

Some estimates of norms of random matrices

We show that for any random matrix with independent mean zero entries

where is some universal constant.