Riesz representation theorem

Let H be a Hilbert space over a field \mathbb{F}, where \mathbb{F} is either the real numbers \R or the complex numbers \Complex. If \mathbb{F} = \Complex (resp. if \mathbb{F} = \R) then H is called a (resp. a ). Every real Hilbert space can be extended to be a dense subset of a unique (up to bijective isometry) complex Hilbert space, called its complexification, which is why Hilbert spaces are often automatically assumed to be complex. Real and complex Hilbert spaces have in common many, but by no means all, properties and results/theorems. This article is intended for both mathematicians and physicists and will describe the theorem for both. In both mathematics and physics, if a Hilbert space is assumed to be real (that is, if \mathbb{F} = \R) then this will usually be made clear. Often in mathematics, and especially in physics, unless indicated otherwise, "Hilbert space" is usually automatically assumed to mean "complex Hilbert space." Depending on the author, in mathematics, "Hilbert space" usually means either (1) a complex Hilbert space, or (2) a real complex Hilbert space. Linear and antilinear maps By definition, an Antilinear map| (also called a ) f : H \to Y is a map between vector spaces that is : f(x + y) = f(x) + f(y) \quad \text{ for all } x, y \in H, and (also called or ): f(c x) = \overline{c} f(x) \quad \text{ for all } x \in H \text{ and all scalar } c \in \mathbb{F}, where \overline{c} is the conjugate of the complex number c = a + b i, given by \overline{c} = a - b i. In contrast, a map f : H \to Y is linear if it is additive and Homogeneous function|: f(c x) = c f(x) \quad \text{ for all } x \in H \quad \text{ and all scalars } c \in \mathbb{F}. Every constant 0 map is always both linear and antilinear. If \mathbb{F} = \R then the definitions of linear maps and antilinear maps are completely identical. A linear map from a Hilbert space into a Banach space (or more generally, from any Banach space into any topological vector space) is continuous if and only if it is bounded; the same is true of antilinear maps. The inverse of any antilinear (resp. linear) bijection is again an antilinear (resp. linear) bijection. The composition of two linear maps is a map. Continuous dual and anti-dual spaces A on H is a function H \to \mathbb{F} whose codomain is the underlying scalar field \mathbb{F}. Denote by H^* (resp. by \overline{H}^*) the set of all continuous linear (resp. continuous antilinear) functionals on H, which is called the (resp. the ) of H. If \mathbb{F} = \R then linear functionals on H are the same as antilinear functionals and consequently, the same is true for such continuous maps: that is, H^* = \overline{H}^*. One-to-one correspondence between linear and antilinear functionals Given any functional f ~:~ H \to \mathbb{F}, the is the functional \begin{alignat}{4} \overline{f} : \,& H && \to \,&& \mathbb{F} \\ & h && \mapsto\,&& \overline{f(h)}. \\ \end{alignat} This assignment is most useful when \mathbb{F} = \Complex because if \mathbb{F} = \R then f = \overline{f} and the assignment f \mapsto \overline{f} reduces down to the identity map. The assignment f \mapsto \overline{f} defines an antilinear bijective correspondence from the set of :all functionals (resp. all linear functionals, all continuous linear functionals H^*) on H, onto the set of :all functionals (resp. all linear functionals, all continuous linear functionals \overline{H}^*) on H. Mathematics vs. physics notations and definitions of inner product The Hilbert space H has an associated inner product H \times H \to \mathbb{F} valued in H's underlying scalar field \mathbb{F} that is linear in one coordinate and antilinear in the other (as specified below). If H is a complex Hilbert space (\mathbb{F} = \Complex), then there is a crucial difference between the notations prevailing in mathematics versus physics, regarding which of the two variables is linear. However, for real Hilbert spaces (\mathbb{F} = \R), the inner product is a symmetric map that is linear in each coordinate (bilinear), so there can be no such confusion. In mathematics, the inner product on a Hilbert space H is often denoted by \left\langle \cdot\,, \cdot \right\rangle or \left\langle \cdot\,, \cdot \right\rangle_H while in physics, the bra–ket notation \left\langle \cdot \mid \cdot \right\rangle or \left\langle \cdot \mid \cdot \right\rangle_H is typically used. In this article, these two notations will be related by the equality: \left\langle x, y \right\rangle := \left\langle y \mid x \right\rangle \quad \text{ for all } x, y \in H.These have the following properties: The map \left\langle \cdot\,, \cdot \right\rangle is linear in its first coordinate; equivalently, the map \left\langle \cdot \mid \cdot \right\rangle is linear in its second coordinate. That is, for fixed y \in H, the map \left\langle \,y\mid \cdot\, \right\rangle = \left\langle \,\cdot\,, y\, \right\rangle : H \to \mathbb{F} with h \mapsto \left\langle \,y\mid h\, \right\rangle = \left\langle \,h, y\, \right\rangle is a linear functional on H. This linear functional is continuous, so \left\langle \,y\mid\cdot\, \right\rangle = \left\langle \,\cdot, y\, \right\rangle \in H^*. The map \left\langle \cdot\,, \cdot \right\rangle is antilinear in its coordinate; equivalently, the map \left\langle \cdot \mid \cdot \right\rangle is antilinear in its coordinate. That is, for fixed y \in H, the map \left\langle \,\cdot\mid y\, \right\rangle = \left\langle \,y, \cdot\, \right\rangle : H \to \mathbb{F} with h \mapsto \left\langle \,h\mid y\, \right\rangle = \left\langle \,y, h\, \right\rangle is an antilinear functional on H. This antilinear functional is continuous, so \left\langle \,\cdot\mid y\, \right\rangle = \left\langle \,y, \cdot\, \right\rangle \in \overline{H}^*. In computations, one must consistently use either the mathematics notation \left\langle \cdot\,, \cdot \right\rangle, which is (linear, antilinear); or the physics notation \left\langle \cdot \mid \cdot \right\rangle, which is (antilinear | linear). Canonical norm and inner product on the dual space and anti-dual space If x = y then \langle \,x\mid x\, \rangle = \langle \,x, x\, \rangle is a non-negative real number and the map \|x\| := \sqrt{\langle x, x \rangle} = \sqrt{\langle x \mid x \rangle} defines a canonical norm on H that makes H into a normed space. As with all normed spaces, the (continuous) dual space H^* carries a canonical norm, called the , that is defined by \|f\|_{H^*} ~:=~ \sup_{\|x\| \leq 1, x \in H} |f(x)| \quad \text{ for every } f \in H^*. The canonical norm on the (continuous) anti-dual space \overline{H}^*, denoted by \|f\|_{\overline{H}^*}, is defined by using this same equation: \|f\|_{\overline{H}^*} ~:=~ \sup_{\|x\| \leq 1, x \in H} |f(x)| \quad \text{ for every } f \in \overline{H}^*. This canonical norm on H^* satisfies the parallelogram law, which means that the polarization identity can be used to define a which this article will denote by the notations \left\langle f, g \right\rangle_{H^*} := \left\langle g \mid f \right\rangle_{H^*}, where this inner product turns H^* into a Hilbert space. There are now two ways of defining a norm on H^*: the norm induced by this inner product (that is, the norm defined by f \mapsto \sqrt{\left\langle f, f \right\rangle_{H^*}}) and the usual dual norm (defined as the supremum over the closed unit ball). These norms are the same; explicitly, this means that the following holds for every f \in H^*: \sup_{\|x\| \leq 1, x \in H} |f(x)| = \|f\|_{H^*} ~=~ \sqrt{\langle f, f \rangle_{H^*}} ~=~ \sqrt{\langle f \mid f \rangle_{H^*}}. As will be described later, the Riesz representation theorem can be used to give an equivalent definition of the canonical norm and the canonical inner product on H^*. The same equations that were used above can also be used to define a norm and inner product on H's anti-dual space \overline{H}^*. Canonical isometry between the dual and antidual The complex conjugate \overline{f} of a functional f, which was defined above, satisfies \|f\|_{H^*} ~=~ \left\|\overline{f}\right\|_{\overline{H}^*} \quad \text{ and } \quad \left\|\overline{g}\right\|_{H^*} ~=~ \|g\|_{\overline{H}^*} for every f \in H^* and every g \in \overline{H}^*. This says exactly that the canonical antilinear bijection defined by \begin{alignat}{4} \operatorname{Cong} :\;&& H^* &&\;\to \;& \overline{H}^* \\[0.3ex] && f &&\;\mapsto\;& \overline{f} \\ \end{alignat} as well as its inverse \operatorname{Cong}^{-1} ~:~ \overline{H}^* \to H^* are antilinear isometries and consequently also homeomorphisms. The inner products on the dual space H^* and the anti-dual space \overline{H}^*, denoted respectively by \langle \,\cdot\,, \,\cdot\, \rangle_{H^*} and \langle \,\cdot\,, \,\cdot\, \rangle_{\overline{H}^*}, are related by \langle \,\overline{f}\, | \,\overline{g}\, \rangle_{\overline{H}^*} = \overline{\langle \,f\, | \,g\, \rangle_{H^*}} = \langle \,g\, | \,f\, \rangle_{H^*} \qquad \text{ for all } f, g \in H^* and \langle \,\overline{f}\, | \,\overline{g}\, \rangle_{H^*} = \overline{\langle \,f\, | \,g\, \rangle_{\overline{H}^*}} = \langle \,g\, | \,f\, \rangle_{\overline{H}^*} \qquad \text{ for all } f, g \in \overline{H}^*. If \mathbb{F} = \R then H^* = \overline{H}^* and this canonical map \operatorname{Cong} : H^* \to \overline{H}^* reduces down to the identity map. == Riesz representation theorem ==

Two vectors x and y are if \langle x, y \rangle = 0, which happens if and only if \|y\| \leq \|y + s x\| for all scalars s. The orthogonal complement of a subset X \subseteq H is X^{\bot} := \{ \,y \in H : \langle y, x \rangle = 0 \text{ for all } x \in X\, \}, which is always a closed vector subspace of H. The Hilbert projection theorem guarantees that for any nonempty closed convex subset C of a Hilbert space there exists a unique vector m \in C such that \|m\| = \inf_{c \in C} \|c\|; that is, m \in C is the (unique) global minimum point of the function C \to [0, \infty) defined by c \mapsto \|c\|. Statement {{Math theorem Let H be a Hilbert space whose inner product \left\langle x, y \right\rangle is linear in its argument and antilinear in its second argument and let \langle y \mid x \rangle := \langle x, y \rangle be the corresponding physics notation. For every continuous linear functional \varphi \in H^*, there exists a unique vector f_{\varphi} \in H, called the such that \varphi(x) = \left\langle x, f_{\varphi} \right\rangle = \left\langle f_\varphi \mid x \right\rangle \quad \text{ for all } x \in H. Importantly for Hilbert spaces, f_{\varphi} is always located in the coordinate of the inner product. H can be written as the direct sum H = K \oplus K^{\bot} (a proof of this is given in the article on the Hilbert projection theorem). Because K \neq H, there exists some non-zero p \in K^{\bot}. For any h \in H, \varphi[(\varphi h) p - (\varphi p) h] ~=~ \varphi[(\varphi h) p] - \varphi[(\varphi p) h] ~=~ (\varphi h) \varphi p - (\varphi p) \varphi h = 0, which shows that (\varphi h) p - (\varphi p) h ~\in~ \ker \varphi = K, where now p \in K^{\bot} implies 0 = \langle \,p\, | \,(\varphi h) p - (\varphi p) h\, \rangle ~=~ \langle \,p\, | \,(\varphi h) p \, \rangle - \langle \,p\, | \,(\varphi p) h\, \rangle ~=~ (\varphi h) \langle \,p\, | \,p \, \rangle - (\varphi p) \langle \,p\, | \,h\, \rangle. Solving for \varphi h shows that \varphi h = \frac{(\varphi p) \langle \,p\, | \,h\, \rangle}{\|p\|^2} = \left\langle \,\frac{\overline{\varphi p}}{\|p\|^2} p\, \Bigg| \,h\, \right\rangle \quad \text{ for every } h \in H, which proves that the vector f_{\varphi} := \frac{\overline{\varphi p}}{\|p\|^2} p satisfies \varphi h = \langle \,f_{\varphi}\, | \,h\, \rangle \text{ for every } h \in H. Applying the norm formula that was proved above with y := f_{\varphi} shows that \|\varphi\|_{H^*} = \left\|\left\langle \,f_{\varphi}\, | \,\cdot\, \right\rangle\right\|_{H^*} = \left\|f_{\varphi}\right\|_H. Also, the vector u := \frac{p}{\|p\|} has norm \|u\| = 1 and satisfies f_{\varphi} := \overline{\varphi(u)} u. \blacksquare It can now be deduced that K^{\bot} is 1-dimensional when \varphi \neq 0. Let q \in K^{\bot} be any non-zero vector. Replacing p with q in the proof above shows that the vector g := \frac{\overline{\varphi q}}{\|q\|^2} q satisfies \varphi(h) = \langle \,g\, | \,h\, \rangle for every h \in H. The uniqueness of the (non-zero) vector f_{\varphi} representing \varphi implies that f_{\varphi} = g, which in turn implies that \overline{\varphi q} \neq 0 and q = \frac{\|q\|^2}{\overline{\varphi q}} f_{\varphi}. Thus every vector in K^{\bot} is a scalar multiple of f_{\varphi}. \blacksquare The formulas for the inner products follow from the polarization identity. Observations If \varphi \in H^* then \varphi \left(f_{\varphi}\right) = \left\langle f_{\varphi}, f_{\varphi} \right\rangle = \left\|f_{\varphi}\right\|^2 = \|\varphi\|^2. So in particular, \varphi \left(f_{\varphi}\right) \geq 0 is always real and furthermore, \varphi \left(f_{\varphi}\right) = 0 if and only if f_{\varphi} = 0 if and only if \varphi = 0. Linear functionals as affine hyperplanes A non-trivial continuous linear functional \varphi is often interpreted geometrically by identifying it with the affine hyperplane A := \varphi^{-1}(1) (the kernel \ker\varphi = \varphi^{-1}(0) is also often visualized alongside A := \varphi^{-1}(1) although knowing A is enough to reconstruct \ker \varphi because if A = \varnothing then \ker \varphi = H and otherwise \ker \varphi = A - A). In particular, the norm of \varphi should somehow be interpretable as the "norm of the hyperplane A". When \varphi \neq 0 then the Riesz representation theorem provides such an interpretation of \|\varphi\| in terms of the affine hyperplane A := \varphi^{-1}(1) as follows: using the notation from the theorem's statement, from \|\varphi\|^2 \neq 0 it follows that C := \varphi^{-1}\left(\|\varphi\|^2\right) = \|\varphi\|^2 \varphi^{-1}(1) = \|\varphi\|^2 A and so \|\varphi\| = \left\|f_{\varphi}\right\| = \inf_{c \in C} \|c\| implies \|\varphi\| = \inf_{a \in A} \|\varphi\|^2 \|a\| and thus \|\varphi\| = \frac{1}{\inf_{a \in A} \|a\|}. This can also be seen by applying the Hilbert projection theorem to A and concluding that the global minimum point of the map A \to [0, \infty) defined by a \mapsto \|a\| is \frac{f_{\varphi}}{\|\varphi\|^2} \in A. The formulas \frac{1}{\inf_{a \in A} \|a\|} = \sup_{a \in A} \frac{1}{\|a\|} provide the promised interpretation of the linear functional's norm \|\varphi\| entirely in terms of its associated affine hyperplane A = \varphi^{-1}(1) (because with this formula, knowing only the A is enough to describe the norm of its associated linear ). Defining \frac{1}{\infty} := 0, the infimum formula \|\varphi\| = \frac{1}{\inf_{a \in \varphi^{-1}(1)} \|a\|} will also hold when \varphi = 0. When the supremum is taken in \R (as is typically assumed), then the supremum of the empty set is \sup \varnothing = - \infty but if the supremum is taken in the non-negative reals [0, \infty) (which is the image/range of the norm \|\,\cdot\,\| when \dim H > 0) then this supremum is instead \sup \varnothing = 0, in which case the supremum formula \|\varphi\| = \sup_{a \in \varphi^{-1}(1)} \frac{1}{\|a\|} will also hold when \varphi = 0 (although the atypical equality \sup \varnothing = 0 is usually unexpected and so risks causing confusion). Constructions of the representing vector Using the notation from the theorem above, several ways of constructing f_{\varphi} from \varphi \in H^* are now described. If \varphi = 0 then f_{\varphi} := 0; in other words, f_0 = 0. This special case of \varphi = 0 is henceforth assumed to be known, which is why some of the constructions given below start by assuming \varphi \neq 0. Orthogonal complement of kernel If \varphi \neq 0 then for any 0 \neq u \in (\ker\varphi)^{\bot}, f_{\varphi} := \frac{\overline{\varphi(u)} u}{\|u\|^2}. If u \in (\ker\varphi)^{\bot} is a unit vector (meaning \|u\| = 1) then f_{\varphi} := \overline{\varphi(u)} u (this is true even if \varphi = 0 because in this case f_{\varphi} = \overline{\varphi(u)} u = \overline{0} u = 0). If u is a unit vector satisfying the above condition then the same is true of -u, which is also a unit vector in (\ker\varphi)^{\bot}. However, \overline{\varphi(-u)} (-u) = \overline{\varphi(u)} u = f_\varphi so both these vectors result in the same f_{\varphi}. Orthogonal projection onto kernel If x \in H is such that \varphi(x) \neq 0 and if x_K is the orthogonal projection of x onto \ker\varphi then f_{\varphi} = \frac{\|\varphi\|^2}{\varphi(x)} \left(x - x_K\right). Orthonormal basis Given an orthonormal basis \left\{e_i\right\}_{i \in I} of H and a continuous linear functional \varphi \in H^*, the vector f_{\varphi} \in H can be constructed uniquely by f_\varphi = \sum_{i \in I} \overline{\varphi\left(e_i\right)} e_i where all but at most countably many \varphi\left(e_i\right) will be equal to 0 and where the value of f_{\varphi} does not actually depend on choice of orthonormal basis (that is, using any other orthonormal basis for H will result in the same vector). If y \in H is written as y = \sum_{i \in I} a_i e_i then \varphi(y) = \sum_{i \in I} \varphi\left(e_i\right) a_i = \langle f_{\varphi} | y \rangle and \left\|f_{\varphi}\right\|^2 = \varphi\left(f_{\varphi}\right) = \sum_{i \in I} \varphi\left(e_i\right) \overline{\varphi\left(e_i\right)} = \sum_{i \in I} \left|\varphi\left(e_i\right)\right|^2 = \|\varphi\|^2. If the orthonormal basis \left\{e_i\right\}_{i \in I} = \left\{e_i\right\}_{i=1}^{\infty} is a sequence then this becomes f_\varphi = \overline{\varphi\left(e_1\right)} e_1 + \overline{\varphi\left(e_2\right)} e_2 + \cdots and if y \in H is written as y = \sum_{i \in I} a_i e_i = a_1 e_1 + a_2 e_2 + \cdots then \varphi(y) = \varphi\left(e_1\right) a_1 + \varphi\left(e_2\right) a_2 + \cdots = \langle f_{\varphi} | y \rangle. Example in finite dimensions using matrix transformations Consider the special case of H = \Complex^n (where n > 0 is an integer) with the standard inner product \langle z \mid w \rangle := \overline{\,\vec{z}\,\,}^{\operatorname{T}} \vec{w} \qquad \text{ for all } \; w, z \in H where w \text{ and } z are represented as column matrices \vec{w} := \begin{bmatrix}w_1 \\ \vdots \\ w_n\end{bmatrix} and \vec{z} := \begin{bmatrix}z_1 \\ \vdots \\ z_n\end{bmatrix} with respect to the standard orthonormal basis e_1, \ldots, e_n on H (here, e_i is 1 at its ith coordinate and 0 everywhere else; as usual, H^* will now be associated with the dual basis) and where \overline{\,\vec{z}\,}^{\operatorname{T}} := \left[\overline{z_1}, \ldots, \overline{z_n}\right] denotes the conjugate transpose of \vec{z}. Let \varphi \in H^* be any linear functional and let \varphi_1, \ldots, \varphi_n \in \Complex be the unique scalars such that \varphi\left(w_1, \ldots, w_n\right) = \varphi_1 w_1 + \cdots + \varphi_n w_n \qquad \text{ for all } \; w := \left(w_1, \ldots, w_n\right) \in H, where it can be shown that \varphi_i = \varphi\left(e_i\right) for all i = 1, \ldots, n. Then the Riesz representation of \varphi is the vector f_{\varphi} ~:=~ \overline{\varphi_1} e_1 + \cdots + \overline{\varphi_n} e_n ~=~ \left(\overline{\varphi_1}, \ldots, \overline{\varphi_n}\right) \in H. To see why, identify every vector w = \left(w_1, \ldots, w_n\right) in H with the column matrix \vec{w} := \begin{bmatrix}w_1 \\ \vdots \\ w_n\end{bmatrix} so that f_{\varphi} is identified with \vec{f_{\varphi}} := \begin{bmatrix}\overline{\varphi_1} \\ \vdots \\ \overline{\varphi_n}\end{bmatrix} = \begin{bmatrix}\overline{\varphi\left(e_1\right)} \\ \vdots \\ \overline{\varphi\left(e_n\right)}\end{bmatrix}. As usual, also identify the linear functional \varphi with its transformation matrix, which is the row matrix \vec{\varphi} := \left[\varphi_1, \ldots, \varphi_n\right] so that \vec{f_{\varphi}} := \overline{\,\vec{\varphi}\,\,}^{\operatorname{T}} and the function \varphi is the assignment \vec{w} \mapsto \vec{\varphi} \, \vec{w}, where the right hand side is matrix multiplication. Then for all w = \left(w_1, \ldots, w_n\right) \in H, \varphi(w) = \varphi_1 w_1 + \cdots + \varphi_n w_n = \left[\varphi_1, \ldots, \varphi_n\right] \begin{bmatrix}w_1 \\ \vdots \\ w_n\end{bmatrix} = \overline{\begin{bmatrix}\overline{\varphi_1} \\ \vdots \\ \overline{\varphi_n}\end{bmatrix}}^{\operatorname{T}} \vec{w} = \overline{\,\vec{f_{\varphi}}\,\,}^{\operatorname{T}} \vec{w} = \left\langle \,\,f_{\varphi}\, \mid \,w\, \right\rangle, which shows that f_{\varphi} satisfies the defining condition of the Riesz representation of \varphi. The bijective antilinear isometry \Phi : H \to H^* defined in the corollary to the Riesz representation theorem is the assignment that sends z = \left(z_1, \ldots, z_n\right) \in H to the linear functional \Phi(z) \in H^* on H defined by w = \left(w_1, \ldots, w_n\right) ~\mapsto~ \langle \,z\, \mid \,w\,\rangle = \overline{z_1} w_1 + \cdots + \overline{z_n} w_n, where under the identification of vectors in H with column matrices and vector in H^* with row matrices, \Phi is just the assignment \vec{z} = \begin{bmatrix}z_1 \\ \vdots \\ z_n\end{bmatrix} ~\mapsto~ \overline{\,\vec{z}\,}^{\operatorname{T}} = \left[\overline{z_1}, \ldots, \overline{z_n}\right]. As described in the corollary, \Phi's inverse \Phi^{-1} : H^* \to H is the antilinear isometry \varphi \mapsto f_{\varphi}, which was just shown above to be: \varphi ~\mapsto~ f_{\varphi} ~:=~ \left(\overline{\varphi\left(e_1\right)}, \ldots, \overline{\varphi\left(e_n\right)}\right); where in terms of matrices, \Phi^{-1} is the assignment \vec{\varphi} = \left[\varphi_1, \ldots, \varphi_n\right] ~\mapsto~ \overline{\,\vec{\varphi}\,\,}^{\operatorname{T}} = \begin{bmatrix}\overline{\varphi_1} \\ \vdots \\ \overline{\varphi_n}\end{bmatrix}. Thus in terms of matrices, each of \Phi : H \to H^* and \Phi^{-1} : H^* \to H is just the operation of conjugate transposition \vec{v} \mapsto \overline{\,\vec{v}\,}^{\operatorname{T}} (although between different spaces of matrices: if H is identified with the space of all column (respectively, row) matrices then H^* is identified with the space of all row (respectively, column matrices). This example used the standard inner product, which is the map \langle z \mid w \rangle := \overline{\,\vec{z}\,\,}^{\operatorname{T}} \vec{w}, but if a different inner product is used, such as \langle z \mid w \rangle_M := \overline{\,\vec{z}\,\,}^{\operatorname{T}} \, M \, \vec{w} \, where M is any Hermitian positive-definite matrix, or if a different orthonormal basis is used then the transformation matrices, and thus also the above formulas, will be different. Relationship with the associated real Hilbert space Assume that H is a complex Hilbert space with inner product \langle \,\cdot\mid\cdot\, \rangle. When the Hilbert space H is reinterpreted as a real Hilbert space then it will be denoted by H_{\R}, where the (real) inner-product on H_{\R} is the real part of H's inner product; that is: \langle x, y \rangle_{\R} := \operatorname{re} \langle x, y \rangle. The norm on H_{\R} induced by \langle \,\cdot\,, \,\cdot\, \rangle_{\R} is equal to the original norm on H and the continuous dual space of H_{\R} is the set of all -valued bounded \R-linear functionals on H_{\R} (see the article about the polarization identity for additional details about this relationship). Let \psi_{\R} := \operatorname{re} \psi and \psi_{i} := \operatorname{im} \psi denote the real and imaginary parts of a linear functional \psi, so that \psi = \operatorname{re} \psi + i \operatorname{im} \psi = \psi_{\R} + i \psi_{i}. The formula expressing a linear functional in terms of its real part is \psi(h) = \psi_{\R}(h) - i \psi_{\R} (i h) \quad \text{ for } h \in H, where \psi_{i}(h) = - i \psi_{\R} (i h) for all h \in H. It follows that \ker\psi_{\R} = \psi^{-1}(i \R), and that \psi = 0 if and only if \psi_{\R} = 0. It can also be shown that \|\psi\| = \left\|\psi_{\R}\right\| = \left\|\psi_i\right\| where \left\|\psi_{\R}\right\| := \sup_{\|h\| \leq 1} \left|\psi_{\R}(h)\right| and \left\|\psi_i\right\| := \sup_{\|h\| \leq 1} \left|\psi_i(h)\right| are the usual operator norms. In particular, a linear functional \psi is bounded if and only if its real part \psi_{\R} is bounded. Representing a functional and its real part The Riesz representation of a continuous linear function \varphi on a complex Hilbert space is equal to the Riesz representation of its real part \operatorname{re} \varphi on its associated real Hilbert space. Explicitly, let \varphi \in H^* and as above, let f_\varphi \in H be the Riesz representation of \varphi obtained in (H, \langle, \cdot, \cdot \rangle), so it is the unique vector that satisfies \varphi(x) = \left\langle f_{\varphi} \mid x \right\rangle for all x \in H. The real part of \varphi is a continuous real linear functional on H_{\R} and so the Riesz representation theorem may be applied to \varphi_{\R} := \operatorname{re} \varphi and the associated real Hilbert space \left(H_{\R}, \langle, \cdot, \cdot \rangle_{\R}\right) to produce its Riesz representation, which will be denoted by f_{\varphi_{\R}}. That is, f_{\varphi_{\R}} is the unique vector in H_{\R} that satisfies \varphi_{\R}(x) = \left\langle f_{\varphi_{\R}} \mid x \right\rangle_{\R} for all x \in H. The conclusion is f_{\varphi_{\R}} = f_{\varphi}. This follows from the main theorem because \ker\varphi_{\R} = \varphi^{-1}(i \R) and if x \in H then \left\langle f_\varphi \mid x \right\rangle_{\R} = \operatorname{re} \left\langle f_\varphi \mid x \right\rangle = \operatorname{re} \varphi(x) = \varphi_{\R}(x) and consequently, if m \in \ker\varphi_{\R} then \left\langle f_{\varphi}\mid m \right\rangle_{\R} = 0, which shows that f_{\varphi} \in (\ker\varphi_{\R})^{\perp_{\R}}. Moreover, \varphi(f_\varphi) = \|\varphi\|^2 being a real number implies that \varphi_{\R} (f_\varphi) = \operatorname{re} \varphi(f_\varphi) = \|\varphi\|^2. In other words, in the theorem and constructions above, if H is replaced with its real Hilbert space counterpart H_{\R} and if \varphi is replaced with \operatorname{re} \varphi then f_{\varphi} = f_{\operatorname{re} \varphi}. This means that vector f_{\varphi} obtained by using \left(H_{\R}, \langle, \cdot, \cdot \rangle_{\R}\right) and the real linear functional \operatorname{re} \varphi is the equal to the vector obtained by using the origin complex Hilbert space \left(H, \left\langle, \cdot, \cdot \right\rangle\right) and original complex linear functional \varphi (with identical norm values as well). Furthermore, if \varphi \neq 0 then f_{\varphi} is perpendicular to \ker\varphi_{\R} with respect to \langle \cdot, \cdot \rangle_{\R} where the kernel of \varphi is be a proper subspace of the kernel of its real part \varphi_{\R}. Assume now that \varphi \neq 0. Then f_{\varphi} \not\in \ker\varphi_{\R} because \varphi_{\R}\left(f_{\varphi}\right) = \varphi\left(f_{\varphi}\right) = \|\varphi\|^2 \neq 0 and \ker\varphi is a proper subset of \ker\varphi_{\R}. The vector subspace \ker \varphi has real codimension 1 in \ker\varphi_{\R}, while \ker\varphi_{\R} has codimension 1 in H_{\R}, and \left\langle f_{\varphi}, \ker\varphi_{\R} \right\rangle_{\R} = 0. That is, f_{\varphi} is perpendicular to \ker\varphi_{\R} with respect to \langle \cdot, \cdot \rangle_{\R}. Canonical injections into the dual and anti-dual Induced linear map into anti-dual The map defined by placing y into the coordinate of the inner product and letting the variable h \in H vary over the coordinate results in an functional: \langle \,\cdot \mid y\, \rangle = \langle \,y, \cdot\, \rangle : H \to \mathbb{F} \quad \text{ defined by } \quad h \mapsto \langle \,h \mid y\, \rangle = \langle \,y, h\, \rangle. This map is an element of \overline{H}^*, which is the continuous anti-dual space of H. The \overline{H}^* is the operator \begin{alignat}{4} \operatorname{In}_H^{\overline{H}^*} :\;&& H &&\;\to \;& \overline{H}^* \\[0.3ex] && y &&\;\mapsto\;& \langle \,\cdot \mid y\, \rangle = \langle \,y, \cdot\, \rangle \\[0.3ex] \end{alignat} which is also an injective isometry. The Fundamental theorem of Hilbert spaces, which is related to Riesz representation theorem, states that this map is surjective (and thus bijective). Consequently, every antilinear functional on H can be written (uniquely) in this form. If \operatorname{Cong} : H^* \to \overline{H}^* is the canonical linear bijective isometry f \mapsto \overline{f} that was defined above, then the following equality holds: \operatorname{Cong} ~\circ~ \operatorname{In}_H^{H^*} ~=~ \operatorname{In}_H^{\overline{H}^*}. == Extending the bra–ket notation to bras and kets ==

Let \left(H, \langle\cdot, \cdot \rangle_H\right) be a Hilbert space and as before, let \langle y\, | \,x \rangle_H := \langle x, y \rangle_H. Let \begin{alignat}{4} \Phi :\;&& H &&\;\to \;& H^* \\[0.3ex] && g &&\;\mapsto\;& \left\langle \,g\mid \cdot\, \right\rangle_H = \left\langle \,\cdot, g\, \right\rangle_H \\ \end{alignat} which is a bijective antilinear isometry that satisfies (\Phi h) g = \langle h\mid g \rangle_H = \langle g, h \rangle_H \quad \text{ for all } g, h \in H. Bras Given a vector h \in H, let \langle h\, | denote the continuous linear functional \Phi h; that is, \langle h\, | ~:=~ \Phi h so that this functional \langle h\, | is defined by g \mapsto \left\langle \,h\mid g\, \right\rangle_H. This map was denoted by \left\langle h \mid \cdot\, \right\rangle earlier in this article. The assignment h \mapsto \langle h | is just the isometric antilinear isomorphism \Phi ~:~ H \to H^*, which is why ~\langle c g + h\, | ~=~ \overline{c} \langle g\mid ~+~ \langle h\, |~ holds for all g, h \in H and all scalars c. The result of plugging some given g \in H into the functional \langle h\, | is the scalar \langle h\, | \,g \rangle_H = \langle g, h \rangle_H, which may be denoted by \langle h \mid g \rangle. Bra of a linear functional Given a continuous linear functional \psi \in H^*, let \langle \psi\mid denote the vector \Phi^{-1} \psi \in H; that is, \langle \psi\mid ~:=~ \Phi^{-1} \psi. The assignment \psi \mapsto \langle \psi\mid is just the isometric antilinear isomorphism \Phi^{-1} ~:~ H^* \to H, which is why ~\langle c \psi + \phi\mid ~=~ \overline{c} \langle \psi\mid ~+~ \langle \phi\mid~ holds for all \phi, \psi \in H^* and all scalars c. The defining condition of the vector \langle \psi | \in H is the technically correct but unsightly equality \left\langle \, \langle \psi\mid \, \mid g \right\rangle_H ~=~ \psi g \quad \text{ for all } g \in H, which is why the notation \left\langle \psi \mid g \right\rangle is used in place of \left\langle \, \langle \psi\mid \, \mid g \right\rangle_H = \left\langle g, \, \langle \psi\mid \right\rangle_H. With this notation, the defining condition becomes \left\langle \psi\mid g \right\rangle ~=~ \psi g \quad \text{ for all } g \in H. Kets For any given vector g \in H, the notation | \,g \rangle is used to denote g; that is, \mid g \rangle : = g. The assignment g \mapsto | \,g \rangle is just the identity map \operatorname{Id}_H : H \to H, which is why ~\mid c g + h \rangle ~=~ c \mid g \rangle ~+~ \mid h \rangle~ holds for all g, h \in H and all scalars c. The notation \langle h\mid g \rangle and \langle \psi\mid g \rangle is used in place of \left\langle h\mid \, \mid g \rangle \, \right\rangle_H ~=~ \left\langle \mid g \rangle, h \right\rangle_H and \left\langle \psi\mid \, \mid g \rangle \, \right\rangle_H ~=~ \left\langle g, \, \langle \psi\mid \right\rangle_H, respectively. As expected, ~\langle \psi\mid g \rangle = \psi g~ and ~\langle h\mid g \rangle~ really is just the scalar ~\langle h\mid g \rangle_H ~=~ \langle g, h \rangle_H. == Adjoints and transposes ==

Let A : H \to Z be a continuous linear operator between Hilbert spaces \left(H, \langle \cdot, \cdot \rangle_H\right) and \left(Z, \langle \cdot, \cdot \rangle_Z \right). As before, let \langle y \mid x \rangle_H := \langle x, y \rangle_H and \langle y \mid x \rangle_Z := \langle x, y \rangle_Z. Denote by \begin{alignat}{4} \Phi_H :\;&& H &&\;\to \;& H^* \\[0.3ex] && g &&\;\mapsto\;& \langle \,g \mid \cdot\, \rangle_H \\ \end{alignat} \quad \text{ and } \quad \begin{alignat}{4} \Phi_Z :\;&& Z &&\;\to \;& Z^* \\[0.3ex] && y &&\;\mapsto\;& \langle \,y \mid \cdot\, \rangle_Z \\ \end{alignat} the usual bijective antilinear isometries that satisfy: \left(\Phi_H g\right) h = \langle g\mid h \rangle_H \quad \text{ for all } g, h \in H \qquad \text{ and } \qquad \left(\Phi_Z y\right) z = \langle y \mid z \rangle_Z \quad \text{ for all } y, z \in Z. Definition of the adjoint For every z \in Z, the scalar-valued map \langle z\mid A (\cdot) \rangle_Z on H defined by h \mapsto \langle z\mid A h \rangle_Z = \langle A h, z \rangle_Z is a continuous linear functional on H and so by the Riesz representation theorem, there exists a unique vector in H, denoted by A^* z, such that \langle z \mid A (\cdot) \rangle_Z = \left\langle A^* z \mid \cdot\, \right\rangle_H, or equivalently, such that \langle z \mid A h \rangle_Z = \left\langle A^* z \mid h \right\rangle_H \quad \text{ for all } h \in H. The assignment z \mapsto A^* z thus induces a function A^* : Z \to H called the of A : H \to Z whose defining condition is \langle z \mid A h \rangle_Z = \left\langle A^* z\mid h \right\rangle_H \quad \text{ for all } h \in H \text{ and all } z \in Z. The adjoint A^* : Z \to H is necessarily a continuous (equivalently, a bounded) linear operator. If H is finite dimensional with the standard inner product and if M is the transformation matrix of A with respect to the standard orthonormal basis then M's conjugate transpose \overline{M^{\operatorname{T}}} is the transformation matrix of the adjoint A^*. Adjoints are transposes It is also possible to define the or of A : H \to Z, which is the map {}^{t}A : Z^* \to H^* defined by sending a continuous linear functionals \psi \in Z^* to {}^{t}A(\psi) := \psi \circ A, where the composition \psi \circ A is always a continuous linear functional on H and it satisfies \|A\| = \left\|{}^t A\right\| (this is true more generally, when H and Z are merely normed spaces). \psi(A h) = \left({}^t A(\psi)\right) h \quad \text{ for all } h \in H \text{ and all } \psi \in Z^*. ---------------- END: Removed info --------------> So for example, if z \in Z then {}^{t}A sends the continuous linear functional \langle z \mid \cdot \rangle_Z \in Z^* (defined on Z by g \mapsto \langle z \mid g \rangle_Z) to the continuous linear functional \langle z \mid A(\cdot) \rangle_Z \in H^* (defined on H by h \mapsto \langle z \mid A(h) \rangle_Z); using bra-ket notation, this can be written as {}^{t}A \langle z \mid ~=~ \langle z \mid A where the juxtaposition of \langle z \mid with A on the right hand side denotes function composition: H \xrightarrow{A} Z \xrightarrow{\langle z \mid} \Complex. The adjoint A^* : Z \to H is actually just to the transpose {}^{t}A : Z^* \to H^* when the Riesz representation theorem is used to identify Z with Z^* and H with H^*. Explicitly, the relationship between the adjoint and transpose is: {{NumBlk|:|{}^{t}A ~\circ~ \Phi_Z ~=~ \Phi_H ~\circ~ A^*||LnSty=1px dashed black}} which can be rewritten as: A^* ~=~ \Phi_H^{-1} ~\circ~ {}^{t}A ~\circ~ \Phi_Z \quad \text{ and } \quad {}^{t}A ~=~ \Phi_H ~\circ~ A^* ~\circ~ \Phi_Z^{-1}. {{Math proof|title=Proof|drop=hidden|proof= To show that {}^{t}A ~\circ~ \Phi_Z ~=~ \Phi_H ~\circ~ A^*, fix z \in Z. The definition of {}^{t}A implies \left({}^{t}A \circ \Phi_Z\right) z = {}^{t}A \left(\Phi_Z z\right) = \left(\Phi_Z z\right) \circ A so it remains to show that \left(\Phi_Z z\right) \circ A = \Phi_H\left(A^* z\right). If h \in H then \left(\left(\Phi_Z z\right) \circ A\right) h = \left(\Phi_Z z\right)(A h) = \langle z\mid A h \rangle_Z = \langle A^* z\mid h \rangle_H = \left(\Phi_H(A^* z)\right) h, as desired. \blacksquare }} Alternatively, the value of the left and right hand sides of () at any given z \in Z can be rewritten in terms of the inner products as: \left({}^{t}A ~\circ~ \Phi_Z\right) z = \langle z \mid A (\cdot) \rangle_Z \quad \text{ and } \quad\left(\Phi_H ~\circ~ A^*\right) z = \langle A^* z\mid\cdot\, \rangle_H so that {}^{t}A ~\circ~ \Phi_Z ~=~ \Phi_H ~\circ~ A^* holds if and only if \langle z \mid A (\cdot) \rangle_Z = \langle A^* z\mid\cdot\, \rangle_H holds; but the equality on the right holds by definition of A^* z. The defining condition of A^* z can also be written \langle z \mid A ~=~ \langle A^*z \mid if bra-ket notation is used. Descriptions of self-adjoint, normal, and unitary operators Assume Z = H and let \Phi := \Phi_H = \Phi_Z. Let A : H \to H be a continuous (that is, bounded) linear operator. Whether or not A : H \to H is self-adjoint, normal, or unitary depends entirely on whether or not A satisfies certain defining conditions related to its adjoint, which was shown by () to essentially be just the transpose {}^t A : H^* \to H^*. Because the transpose of A is a map between continuous linear functionals, these defining conditions can consequently be re-expressed entirely in terms of linear functionals, as the remainder of subsection will now describe in detail. The linear functionals that are involved are the simplest possible continuous linear functionals on H that can be defined entirely in terms of A, the inner product \langle \,\cdot\mid\cdot\, \rangle on H, and some given vector h \in H. Specifically, these are \left\langle A h\mid\cdot\, \right\rangle and \langle h\mid A (\cdot) \rangle where \left\langle A h\mid\cdot\, \right\rangle = \Phi (A h) = (\Phi \circ A) h \quad \text{ and } \quad \langle h\mid A (\cdot) \rangle = \left({}^{t}A \circ \Phi\right) h. Self-adjoint operators A continuous linear operator A : H \to H is called self-adjoint if it is equal to its own adjoint; that is, if A = A^*. Using (), this happens if and only if: \Phi \circ A = {}^t A \circ \Phi where this equality can be rewritten in the following two equivalent forms: A = \Phi^{-1} \circ {}^t A \circ \Phi \quad \text{ or } \quad {}^{t}A = \Phi \circ A \circ \Phi^{-1}. Unraveling notation and definitions produces the following characterization of self-adjoint operators in terms of the aforementioned continuous linear functionals: A is self-adjoint if and only if for all z \in H, the linear functional \langle z\mid A (\cdot) \rangle is equal to the linear functional \langle A z\mid\cdot\, \rangle; that is, if and only if {{NumBlk|:|\langle z\mid A (\cdot) \rangle = \langle A z\mid \cdot\, \rangle \quad \text{ for all } z \in H||LnSty=1px dashed black}} where if bra-ket notation is used, this is \langle z \mid A ~=~ \langle A z \mid \quad \text{ for all } z \in H. Normal operators A continuous linear operator A : H \to H is called normal if A A^* = A^* A, which happens if and only if for all z, h \in H, \left\langle A A^* z\mid h \right\rangle = \left\langle A^* A z\mid h \right\rangle. Using () and unraveling notation and definitions produces the following characterization of normal operators in terms of inner products of continuous linear functionals: A is a normal operator if and only if {{NumBlk|:|\left\langle \,\langle A h \mid\cdot\, \rangle\mid\langle A z \mid\cdot\, \rangle\, \right\rangle_{H^*} ~=~ \left\langle \,\langle h | A(\cdot) \rangle\mid\langle z \mid A(\cdot) \rangle\, \right\rangle_{H^*} \quad \text{ for all } z, h \in H||LnSty=1px dashed black}} where the left hand side is also equal to \overline{\langle A h \mid A z \rangle}_H = \langle A z \mid A h \rangle_H. The left hand side of this characterization involves only linear functionals of the form \langle A h \mid\cdot\, \rangle while the right hand side involves only linear functions of the form \langle h \mid A(\cdot) \rangle (defined as above). So in plain English, characterization () says that an operator is normal when the inner product of any two linear functions of the first form is equal to the inner product of their second form (using the same vectors z, h \in H for both forms). In other words, if it happens to be the case (and when A is injective or self-adjoint, it is) that the assignment of linear functionals \langle A h \mid\cdot\, \rangle ~\mapsto~ \langle h | A(\cdot) \rangle is well-defined (or alternatively, if \langle h | A(\cdot) \rangle ~\mapsto~ \langle A h \mid\cdot\, \rangle is well-defined) where h ranges over H, then A is a normal operator if and only if this assignment preserves the inner product on H^*. The fact that every self-adjoint bounded linear operator is normal follows readily by direct substitution of A^* = A into either side of A^* A = A A^*. This same fact also follows immediately from the direct substitution of the equalities () into either side of (). Alternatively, for a complex Hilbert space, the continuous linear operator A is a normal operator if and only if \|Az\| = \left\|A^* z\right\| for every z \in H, which happens if and only if \|Az\|_H = \|\langle z\, | \,A(\cdot) \rangle\|_{H^*} \quad \text{ for every } z \in H. Unitary operators An invertible bounded linear operator A : H \to H is said to be unitary if its inverse is its adjoint: A^{-1} = A^*. By using (), this is seen to be equivalent to \Phi \circ A^{-1} = {}^{t}A \circ \Phi. Unraveling notation and definitions, it follows that A is unitary if and only if \langle A^{-1} z\mid\cdot\, \rangle = \langle z\mid A (\cdot) \rangle \quad \text{ for all } z \in H. The fact that a bounded invertible linear operator A : H \to H is unitary if and only if A^* A = \operatorname{Id}_H (or equivalently, {}^t A \circ \Phi \circ A = \Phi) produces another (well-known) characterization: an invertible bounded linear map A is unitary if and only if \langle A z\mid A (\cdot)\, \rangle = \langle z\mid\cdot\, \rangle \quad \text{ for all } z \in H. Because A : H \to H is invertible (and so in particular a bijection), this is also true of the transpose {}^t A : H^* \to H^*. This fact also allows the vector z \in H in the above characterizations to be replaced with A z or A^{-1} z, thereby producing many more equalities. Similarly, \,\cdot\, can be replaced with A(\cdot) or A^{-1}(\cdot). ==See also==