Functional analysis is a branch of mathematical analysis, the core of which is formed by the study of vector spaces endowed with some kind of limit-related structure (for example, inner product, norm, or topology) and the linear functions defined on these spaces and suitably respecting these structures. The historical roots of functional analysis lie in the study of spaces of functions and the formulation of properties of transformations of functions such as the Fourier transform as transformations defining, for example, continuous or unitary operators between function spaces. This point of view turned out to be particularly useful for the study of differential and integral equations. The usage of the word functional as a noun goes back to the calculus of variations, implying a function whose argument is a function. The term was first used in Hadamard's 1910 book on that subject. However, the general concept of a functional had previously been introduced in 1887 by the Italian mathematician and physicist Vito Volterra. The theory of nonlinear functionals was continued by students of Hadamard, in particular Fréchet and Lévy. Hadamard also founded the modern school of linear functional analysis further developed by Riesz and the group of Polish mathematicians around Stefan Banach. In modern introductory texts on functional analysis, the subject is seen as the study of vector spaces endowed with a topology, in particular infinite-dimensional spaces. In contrast, linear algebra deals mostly with finite-dimensional spaces, and does not use topology. An important part of functional analysis is the extension of the theories of measure, integration, and probability to infinite-dimensional spaces, also known as infinite dimensional analysis.
Normed vector spaces The basic and historically first class of spaces studied in functional analysis are complete normed vector spaces over the real or complex numbers. Such spaces are called Banach spaces. An important example is a Hilbert space, where the norm arises from an inner product. These spaces are of fundamental importance in many areas, including the mathematical formulation of quantum mechanics, machine learning, partial differential equations, and Fourier analysis. More generally, functional analysis includes the study of Fréchet spaces and other topological vector spaces not endowed with a norm. An important object of study in functional analysis are the continuous linear operators defined on Banach and Hilbert spaces. These lead naturally to the definition of C*-algebras and other operator algebras.
Hilbert spaces Hilbert spaces can be completely classified: there is a unique Hilbert space up to isomorphism for every cardinality of the orthonormal basis. Finite-dimensional Hilbert spaces are fully understood in linear algebra, and infinite-dimensional separable Hilbert spaces are isomorphic to ℓ 2 ( ℵ 0 ) {\displaystyle \ell ^{\,2}(\aleph _{0})\,} . Separability being important for applications, functional analysis of Hilbert spaces consequently mostly deals with this space. One of the open problems in functional analysis is to prove that every bounded linear operator on a Hilbert space has a proper invariant subspace. Many special cases of this invariant subspace problem have already been proven.
Banach spaces General Banach spaces are more complicated than Hilbert spaces, and cannot be classified in such a simple manner as those. In particular, many Banach spaces lack a notion analogous to an orthonormal basis. Examples of Banach spaces are L p {\displaystyle L^{p}} -spaces for any real number p ≥ 1 {\displaystyle p\geq 1} . Given also a measure μ {\displaystyle \mu } on set X {\displaystyle X} , then L p ( X ) {\displaystyle L^{p}(X)} , sometimes also denoted L p ( X , μ ) {\displaystyle L^{p}(X,\mu )} or L p ( μ ) {\displaystyle L^{p}(\mu )} , has as its vectors equivalence classes [ f ] {\displaystyle [\,f\,]} of measurable functions whose absolute value's p {\displaystyle p} -th power has finite integral; that is, functions f {\displaystyle f} for which one has
∫ X | f ( x ) | p d μ ( x ) < ∞ . {\displaystyle \int _{X}\left|f(x)\right|^{p}\,d\mu (x)<\infty .}
If μ {\displaystyle \mu } is the counting measure, then the integral may be replaced by a sum. That is, we require
∑ x ∈ X | f ( x ) | p < ∞ . {\displaystyle \sum _{x\in X}\left|f(x)\right|^{p}<\infty .}
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