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We show that if (Ω, Σ, μ) and (Ω′, Σ′, μ′) are probability spaces, then every regular operator T : Lp(μ) → Lq(μ′), 1 < p < ∞, 1 ≤ q < ∞, is thin if and only if it is strictly singular. We also show that if 0 ≤ S ≤ T : Lp(μ) → Lq(μ′), then T thin implies S is thin. We extend these results to some Köthe function spaces.
The theme of this chapter is control theory. We discuss what it means to say that a linear system is stable, and then present some of the themes of H∞ control theory, presented from an operator-theoretic point of view.
One of the main aims of modern control theory is to achieve robustness, that is, the stabilization of a system subject to perturbations, measurement errors, and the like. In order to study this we require a measure of the distance between systems, and it turns out that the operator gap is the “correct” one to use. Another way of measuring distances, the so-called chordal metric between meromorphic functions, turns out to be closely related.
Stability theory
The basic signal spaces in this chapter are vector-valued L2(0, ∞) or ℓ2(ℤ+) spaces, and we are concerned with shift-invariant input–output operators T. Our first result shows that, if the domain of such an operator is the whole space, then it is necessarily bounded (a result in automatic continuity theory).
Theorem 4.1.1Let T: L2(0, ∞; ℂm) → L2(0, ∞; ℂp) be an operator commuting with the right shift Rλfor some λ > 0. Then T is bounded.
Proof: It is sufficient to prove the result for m = p = 1, since in general T may be represented by a p × m matrix of shift-invariant operators from L2(0, ∞) to itself.
So far we have worked almost entirely with signal spaces of the form ℓ2(ℤ+) or L2(0, ∞) and their full-axis analogues; in physical terms, these are spaces of finite-energy signals, which die away in some sense at infinity. In this chapter we shall work with what may loosely be described as finite-power signals or, still more loosely, as persistent signals.
Persistent signals include classes of signals with some regularity properties, such as periodic and almost-periodic signals, as well as much more general spaces of signals in which the notion of “power” is less clearly defined. In particular, we are are able to discuss concepts such as the idea of a white noise signal in a rigorous and largely non-stochastic framework. Persistent signals in general can be taken as the inputs and outputs of linear systems (the term filter is commonly used here), as we shall see.
Almost-periodic functions
Almost-periodic functions defined on the real line form a class of functions that has been much studied since the 1920s. Our aim in this section is to derive their fundamental properties and to bring out their similarities with the theory of periodic functions.
It should be emphasised at the start that this book does not claim to be an exhaustive treatise on either linear operators or linear systems, but it presents an introduction to the common ground between the two subjects, one pure mathematical and one applied, by regarding a linear system as a (causal) shift-invariant operator on a Hilbert space such as ℓ2(ℤ+) or L2(0, ∞). It therefore includes material on Hardy spaces, shift-invariant operators, the commutant lifting theorem, and almost-periodic functions, which might traditionally be regarded as “pure” mathematics, and is suitable for those working in analysis who wish to learn more advanced material on linear operators.
At the same time, it is hoped that students and researchers in systems and control will find the approach taken attractive, including as it does much recent material on the mathematical side of systems theory, which cannot easily be found elsewhere: these include recent developments in robust control, power signal spaces, and the input–output approach to time-delay systems. Parts of this book have been expounded in graduate courses and other lectures at that level and could be used for a similar purpose elsewhere.
Chapter 1 begins with a review of basic operator theory without proofs. All this material can be found in any introductory course and many textbooks, and so is included mostly for reference. The other main topic of this chapter, which is treated in considerably more detail, is that of Hardy spaces, which are Banach spaces of analytic functions on the disc or half-plane.
How ‘tightly’ can we pack a given number of $r$-sets of an $n$-set? To be a little more precise, let $X=[n]=\{ 1,\ldots,n \}$, and let $X^r=\{ A\subset X : |A|=r \}$. For a set system $\mathcal{A}\subset X^r $, the neighbourhood of $\mathcal{A}$ is $N(\mathcal{A})=\{ B \in X^r: |B \bigtriangleup A|\le 2 \hbox{ for some }A \in \mathcal{A} \}$. In other words, $N(\mathcal{A})$ consists of those $r$-sets that are either in $\mathcal{A}$ or are ‘adjacent’ to it, in the sense that they are at minimal Hamming distance (i.e., distance 2) from some point of it. Given $|\mathcal{A}|$, how small can $|N(\mathcal{A})|$ be?
Sampling formulas describe probability laws of exchangeable combinatorial structures like partitions and compositions. We give a brief account of two known parametric families of sampling formulas for compositions and add a new family to the list.
We give a quantitative proof that, for sufficiently large $N$, every subset of $[N]^2$ of size at least $\delta N^2$ contains a square, i.e., four points with coordinates $\{(a,b),(a+d,b),(a,b+d),(a+d,b+d)\}$.
Baranyai's partition theorem states that the edges of the complete $r$-graph on $n$ vertices can be partitioned into $1$-factors provided that $r$ divides $n$. Fon-der-Flaass has conjectured that for $r=3$ such a partitioning exists with the property that any two $1$-factors are ‘far apart’ in some natural sense.
Our aim in this note is to prove that the Fon-der-Flaass conjecture is not always true: it fails for $n=12$. Our methods are based on some new ‘auxiliary’ hypergraphs.
Given a set $L$ of $n$ lines in ${\mathbb R}^3$, joints are points in ${\mathbb R}^3$ that are incident to at least three non-coplanar lines in $L$. We show that there are at most $O(n^{5/3})$ incidences between $L$ and the set of its joints.
This result leads to related questions about incidences between $L$ and a set $P$ of $m$ points in ${\mathbb R}^3$. First, we associate with every point $p \in P$ the minimum number of planes it takes to cover all lines incident to $p$. Then the sum of these numbers is at most \[ O\big(m^{4/7}n^{5/7}+m+n\big).\] Second, if each line forms a fixed given non-zero angle with the $xy$-plane – we say the lines are equally inclined – then the number of (real) incidences is at most \[ O\big(\min\big\{m^{3/4}n^{1/2}\kappa(m),\ m^{4/7}n^{5/7}\big\} + m + n\big) , \] where $\kappa(m) \,{=}\, (\log m)^{O(\alpha^2(m))}$, and $\alpha(m)$ is the slowly growing inverse Ackermann function. These bounds are smaller than the tight Szemerédi–Trotter bound for point–line incidences in $\reals^2$, unless both bounds are linear. They are the first results of this type on incidences between points and $1$-dimensional objects in $\reals^3$. This research was stimulated by a question raised by G. Elekes.
I show that the zeros of the chromatic polynomials $P_G(q)$ for the generalized theta graphs $\Theta^{(s,p)}$ are, taken together, dense in the whole complex plane with the possible exception of the disc $|q-1| < 1$. The same holds for their dichromatic polynomials (alias Tutte polynomials, alias Potts-model partition functions) $Z_G(q,v)$ outside the disc $|q+v| < |v|$. An immediate corollary is that the chromatic roots of not-necessarily-planar graphs are dense in the whole complex plane. The main technical tool in the proof of these results is the Beraha–Kahane–Weiss theorem on the limit sets of zeros for certain sequences of analytic functions, for which I give a new and simpler proof.
Bollobás and Riordan introduce a Tutte polynomial for coloured graphs and matroids in [3]. We observe that this polynomial has an expansion as a sum indexed by the subsets of the ground-set of a coloured matroid, generalizing the subset expansion of the Tutte polynomial. We also discuss similar expansions of other contraction–deletion invariants of graphs and matroids.