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Guided by basic intuitions, we introduce the notion of a complete metric space and discover that we have in fact encountered it before in our study of mathematics. In particular, we learn that if the set of real numbers were not complete, bounded increasing (or decreasing) sequences would not have limits. Similarly, we realize that if time were not complete, Achilles would never catch the tortoise. In a slightly more advanced part, we show that criteria for convergence of functional series involve the notion of completeness of the space of continuous functions.
Although there is a particular beauty in the statement that the space of bounded linear operators in a Banach space is itself a Banach space, the norm in this space is more often than not too strong to encompass more delicate convergence theorems of contemporary mathematics. Strong convergence is a notion that is more suitable for such purposes. We exemplify this by studying two classical theorems: Bernstein’s approximation of continuous functions by polynomials and the theorem of Fej\’er on convergence of Fourier series. In both cases the operators involved converge strongly but not in the operator norm. Before doing that, however, we discuss the theorem of Banach and Steinhaus. This result ensures in particular that in Banach spaces strong convergence of bounded linear operators implies boundedness of the limit operator. The chapter also covers the famous Poisson approximation to the binomial, the only example of a limit theorem of probability known to the author that can in fact be stated in the framework of norm convergence of operators.
The Riemann integral for vector-valued functions can be defined in the same way as for scalar-valued functions. Moreover, the theory of the so-defined integral is rather similar to the classical one. In particular, any continuous function with values in a Banach space is Riemann integrable, and the fundamental theorem of calculus remains valid. The theory of Riemann integration for functions with values in a normed, not complete, space would not be so elegant.
A Hilbert space is specific example of a Banach space, because its norm comes from a scalar product. This particular norm makes the geometry of a Hilbert space very familiar to us. In particular, in a Hilbert space one can find a unique element of a closed, convex subset that minimizes the distance of this subset from a point lying outside of it. One can also think of projections of vectors on closed subspaces. Again, all this would have been impossible were the space with scalar product not complete. The chapter ends with remarkable example showing that conditional probability, one of the fundamental notions of probability theory, has much to do with projections in a Hilbert space.
Banach’s principle states that if a map T uniformly reduces the distance between points of a complete metric space, then there is a unique x such that Tx = x, called T’s fixed point. This simple statement has profound and surprising consequences, as we will see in the following chapters. For now, we will content ourselves with an example, which may appear to belong to the realm of linear algebra, but is, in fact, much easier to deal with using metric notions.
The celebrated Baire’s category theorem says that a complete space cannot be represented as a countable union of nowhere dense sets. This is a fundamental description of the structure of complete spaces. Because of this, it is fitting to derive the Banach–Steinhaus theorem as a consequence of Baire’s. This is what we do at the beginning of this chapter. We also show that the set of differentiable functions is quite small (i.e. meagre) in the space of continuous functions. As further consequences of Baire’s theorem we discuss two other fundamental results of functional analysis – the open mapping theorem and the closed graph theorem – together with some of their most immediate applications. In the meantime, we use the Banach–Steinhaus theorem to show that a Fourier series cannot converge uniformly for all continuous (and periodic) functions.
A normed linear space can be (uniquely) completed to a Banach space. However, whereas a Banach space is a match for its practically unique norm, there are many possible norms that can be used in a linear space, and depending on a choice of norm we obtain many different completions of a single space. This phenomenon is discussed first in the case of the space of sequences that have all but a finite number of coordinates equal to zero, and in the case of the space of polynomials. The injective and projective tensor norms, which show up naturally in the tensor product of two simple sequence spaces, illustrate this principle further, but they have their own importance, reaching far beyond the scope of the book.
In Cartan’s PhD thesis, there is a formula defining a certain rank 8 vector distribution in dimension 15, whose algebra of authomorphism is the split real form of the simple exceptional complex Lie algebra $\mathfrak {f}_4$. Cartan’s formula is written in the standard Cartesian coordinates in $\mathbb {R}^{15}$. In the present paper, we explain how to find analogous formulae for the flat models of any bracket generating distribution $\mathcal D$ whose symbol algebra $\mathfrak {n}({\mathcal D})$ is constant and 2-step graded, $\mathfrak {n}({\mathcal D})=\mathfrak {n}_{-2}\oplus \mathfrak {n}_{-1}$.
The formula is given in terms of a solution to a certain system of linear algebraic equations determined by two representations $(\rho ,\mathfrak {n}_{-1})$ and $(\tau ,\mathfrak {n}_{-2})$ of a Lie algebra $\mathfrak {n}_{00}$ contained in the $0$th order Tanaka prolongation $\mathfrak {n}_0$ of $\mathfrak {n}({\mathcal D})$.
Numerous examples are provided, with particular emphasis put on the distributions with symmetries being real forms of simple exceptional Lie algebras $\mathfrak {f}_4$ and $\mathfrak {e}_6$.
We study pencils of curves on a germ of complex reduced surface $(S,0)$. These are families of curves parametrized by $ \mathbb{P}^1 $ having 0 as the unique common point. We prove that for $w\in \mathbb{P}^1$, the corresponding curve of the pencil does not have the generic topology if and only if either the corresponding curve of the pulled-back pencil to the normalized surface has a non generic topology or w is a limit value for the function $ f/g $ along the singular locus of $(S,0)$, where f and g are generators of the pencil.
In this paper, we give necessary and sufficient conditions for the rigidity of the perimeter inequality under Schwarz symmetrization. The term rigidity refers to the situation in which the equality cases are only obtained by translations of the symmetric set. In particular, we prove that the sufficient conditions for rigidity provided in M. Barchiesi, F. Cagnetti and N. Fusco [Stability of the Steiner symmetrization of convex sets. J. Eur. Math. Soc. 15 (2013), 1245-1278.] are also necessary.
We give a simple method to estimate the number of distinct copies of some classes of spanning subgraphs in hypergraphs with a high minimum degree. In particular, for each $k\geq 2$ and $1\leq \ell \leq k-1$, we show that every $k$-graph on $n$ vertices with minimum codegree at least
contains $\exp\!(n\log n-\Theta (n))$ Hamilton $\ell$-cycles as long as $(k-\ell )\mid n$. When $(k-\ell )\mid k$, this gives a simple proof of a result of Glock, Gould, Joos, Kühn, and Osthus, while when $(k-\ell )\nmid k$, this gives a weaker count than that given by Ferber, Hardiman, and Mond, or when $\ell \lt k/2$, by Ferber, Krivelevich, and Sudakov, but one that holds for an asymptotically optimal minimum codegree bound.
For $\lambda \in (0,\,1/2]$ let $K_\lambda \subset \mathbb {R}$ be a self-similar set generated by the iterated function system $\{\lambda x,\, \lambda x+1-\lambda \}$. Given $x\in (0,\,1/2)$, let $\Lambda (x)$ be the set of $\lambda \in (0,\,1/2]$ such that $x\in K_\lambda$. In this paper we show that $\Lambda (x)$ is a topological Cantor set having zero Lebesgue measure and full Hausdorff dimension. Furthermore, we show that for any $y_1,\,\ldots,\, y_p\in (0,\,1/2)$ there exists a full Hausdorff dimensional set of $\lambda \in (0,\,1/2]$ such that $y_1,\,\ldots,\, y_p \in K_\lambda$.
All vital functions of living cells rely on the production of various functional molecules through gene expression. The production periods are burst-like and stochastic due to the discrete nature of biochemical reactions. In certain contexts, the concentrations of RNA or protein require regulation to maintain a fine internal balance within the cell. Here we consider a motif of two types of RNA molecules – mRNA and an antagonistic microRNA – which are encoded by a shared coding sequence and form a feed forward loop (FFL). This control mechanism is shown to be perfectly adapting in the deterministic context. We demonstrate that the adaptation (of the mean value) becomes imperfect if production occurs in random bursts. The FFL nevertheless outperforms the benchmark feedback loop in terms of counterbalancing variations in the signal. Methodologically, we adapt a hybrid stochastic model, which has widely been used to model a single regulatory molecule, to the current case of a motif involving two species; the use of the Laplace transform thereby circumvents the problem of moment closure that arises owing to the mRNA–microRNA interaction. We expect that the approach can be applicable to other systems with nonlinear kinetics.
A linear equation $E$ is said to be sparse if there is $c\gt 0$ so that every subset of $[n]$ of size $n^{1-c}$ contains a solution of $E$ in distinct integers. The problem of characterising the sparse equations, first raised by Ruzsa in the 90s, is one of the most important open problems in additive combinatorics. We say that $E$ in $k$ variables is abundant if every subset of $[n]$ of size $\varepsilon n$ contains at least $\text{poly}(\varepsilon )\cdot n^{k-1}$ solutions of $E$. It is clear that every abundant $E$ is sparse, and Girão, Hurley, Illingworth, and Michel asked if the converse implication also holds. In this note, we show that this is the case for every $E$ in four variables. We further discuss a generalisation of this problem which applies to all linear equations.