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Numerical range

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In the mathematical field of linear algebra and convex analysis, the numerical range or field of values or Wertvorrat or Wertevorrat of a complex n × n {\displaystyle n\times n} {\displaystyle n\times n} matrix A is the set

W ( A ) = { x A x x x x C n ,   x 0 } = { x , A x x C n ,   x 2 = 1 } {\displaystyle W(A)=\left\{{\frac {\mathbf {x} ^{*}A\mathbf {x} }{\mathbf {x} ^{*}\mathbf {x} }}\mid \mathbf {x} \in \mathbb {C} ^{n},\ \mathbf {x} \neq 0\right\}=\left\{\langle \mathbf {x} ,A\mathbf {x} \rangle \mid \mathbf {x} \in \mathbb {C} ^{n},\ \|\mathbf {x} \|_{2}=1\right\}} {\displaystyle W(A)=\left\{{\frac {\mathbf {x} ^{*}A\mathbf {x} }{\mathbf {x} ^{*}\mathbf {x} }}\mid \mathbf {x} \in \mathbb {C} ^{n},\ \mathbf {x} \neq 0\right\}=\left\{\langle \mathbf {x} ,A\mathbf {x} \rangle \mid \mathbf {x} \in \mathbb {C} ^{n},\ \|\mathbf {x} \|_{2}=1\right\}}

where x {\displaystyle \mathbf {x} ^{*}} {\displaystyle \mathbf {x} ^{*}} denotes the conjugate transpose of the vector x {\displaystyle \mathbf {x} } {\displaystyle \mathbf {x} }. The numerical range includes, in particular, the diagonal entries of the matrix (obtained by choosing x equal to the unit vectors along the coordinate axes) and the eigenvalues of the matrix (obtained by choosing x equal to the eigenvectors).

Equivalently, the elements of W ( A ) {\textstyle W(A)} {\textstyle W(A)} are of the form tr ( A P ) {\textstyle \operatorname {tr} (AP)} {\textstyle \operatorname {tr} (AP)}, where P {\textstyle P} {\textstyle P} is a Hermitian projection operator from C 2 {\textstyle \mathbb {C} ^{2}} {\textstyle \mathbb {C} ^{2}} to a one-dimensional subspace.

In engineering, numerical ranges are used as a rough estimate of eigenvalues of A. Recently, generalizations of the numerical range are used to study quantum computing.

A related concept is the numerical radius, which is the largest absolute value of the numbers in the numerical range, i.e.

r ( A ) = sup { | λ | : λ W ( A ) } = sup x 2 = 1 | x , A x | . {\displaystyle r(A)=\sup\{|\lambda |:\lambda \in W(A)\}=\sup _{\|x\|_{2}=1}|\langle \mathbf {x} ,A\mathbf {x} \rangle |.} {\displaystyle r(A)=\sup\{|\lambda |:\lambda \in W(A)\}=\sup _{\|x\|_{2}=1}|\langle \mathbf {x} ,A\mathbf {x} \rangle |.}

Properties

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Let sum of sets denote a sumset.

General properties

  1. The numerical range is the range of the Rayleigh quotient.
  2. (Hausdorff–Toeplitz theorem) The numerical range is convex and compact.
  3. W ( α A + β I ) = α W ( A ) + { β } {\displaystyle W(\alpha A+\beta I)=\alpha W(A)+\{\beta \}} {\displaystyle W(\alpha A+\beta I)=\alpha W(A)+\{\beta \}} for all square matrix A {\displaystyle A} {\displaystyle A} and complex numbers α {\displaystyle \alpha } {\displaystyle \alpha } and β {\displaystyle \beta } {\displaystyle \beta }. Here I {\displaystyle I} {\displaystyle I} is the identity matrix.
  4. W ( A ) {\displaystyle W(A)} {\displaystyle W(A)} is a subset of the closed right half-plane if and only if A + A {\displaystyle A+A^{*}} {\displaystyle A+A^{*}} is positive semidefinite.
  5. The numerical range W ( ) {\displaystyle W(\cdot )} {\displaystyle W(\cdot )} is the only function on the set of square matrices that satisfies (2), (3) and (4).
  6. W ( U A U ) = W ( A ) {\displaystyle W(UAU^{*})=W(A)} {\displaystyle W(UAU^{*})=W(A)} for any unitary U {\displaystyle U} {\displaystyle U}.
  7. W ( A ) = W ( A ) {\displaystyle W(A^{*})=W(A)^{*}} {\displaystyle W(A^{*})=W(A)^{*}}.
  8. If A {\displaystyle A} {\displaystyle A} is Hermitian, then W ( A ) {\displaystyle W(A)} {\displaystyle W(A)} is on the real line. If A {\displaystyle A} {\displaystyle A} is anti-Hermitian, then W ( A ) {\displaystyle W(A)} {\displaystyle W(A)} is on the imaginary line.
  9. W ( A ) = { z } {\displaystyle W(A)=\{z\}} {\displaystyle W(A)=\{z\}} if and only if A = z I {\displaystyle A=zI} {\displaystyle A=zI}.
  10. (Sub-additive) W ( A + B ) W ( A ) + W ( B ) {\displaystyle W(A+B)\subseteq W(A)+W(B)} {\displaystyle W(A+B)\subseteq W(A)+W(B)}.
  11. W ( A ) {\displaystyle W(A)} {\displaystyle W(A)} contains all the eigenvalues of A {\displaystyle A} {\displaystyle A}.
  12. The numerical range of a 2 × 2 {\displaystyle 2\times 2} {\displaystyle 2\times 2} matrix is a filled ellipse.
  13. W ( A ) {\displaystyle W(A)} {\displaystyle W(A)} is a real line segment [ α , β ] {\displaystyle [\alpha ,\beta ]} {\displaystyle [\alpha ,\beta ]} if and only if A {\displaystyle A} {\displaystyle A} is a Hermitian matrix with its smallest and the largest eigenvalues being α {\displaystyle \alpha } {\displaystyle \alpha } and β {\displaystyle \beta } {\displaystyle \beta }.

Normal matrices

  1. If A {\textstyle A} {\textstyle A} is normal, and x span ( v 1 , , v k ) {\textstyle x\in \operatorname {span} (v_{1},\dots ,v_{k})} {\textstyle x\in \operatorname {span} (v_{1},\dots ,v_{k})}, where v 1 , , v k {\textstyle v_{1},\ldots ,v_{k}} {\textstyle v_{1},\ldots ,v_{k}} are eigenvectors of A {\textstyle A} {\textstyle A} corresponding to λ 1 , , λ k {\textstyle \lambda _{1},\ldots ,\lambda _{k}} {\textstyle \lambda _{1},\ldots ,\lambda _{k}}, respectively, then x , A x hull ( λ 1 , , λ k ) {\textstyle \langle x,Ax\rangle \in \operatorname {hull} \left(\lambda _{1},\ldots ,\lambda _{k}\right)} {\textstyle \langle x,Ax\rangle \in \operatorname {hull} \left(\lambda _{1},\ldots ,\lambda _{k}\right)}.
  2. If A {\displaystyle A} {\displaystyle A} is a normal matrix then W ( A ) {\displaystyle W(A)} {\displaystyle W(A)} is the convex hull of its eigenvalues.
  3. If α {\displaystyle \alpha } {\displaystyle \alpha } is a sharp point on the boundary of W ( A ) {\displaystyle W(A)} {\displaystyle W(A)}, then α {\displaystyle \alpha } {\displaystyle \alpha } is a normal eigenvalue of A {\displaystyle A} {\displaystyle A}.

Numerical radius

  1. r ( ) {\displaystyle r(\cdot )} {\displaystyle r(\cdot )} is a unitarily invariant norm on the space of n × n {\displaystyle n\times n} {\displaystyle n\times n} matrices.
  2. r ( A ) A op 2 r ( A ) {\displaystyle r(A)\leq \|A\|_{\operatorname {op} }\leq 2r(A)} {\displaystyle r(A)\leq \|A\|_{\operatorname {op} }\leq 2r(A)}, where op {\displaystyle \|\cdot \|_{\operatorname {op} }} {\displaystyle \|\cdot \|_{\operatorname {op} }} denotes the operator norm.[1] [2] [3] [4]
  3. r ( A ) = A op {\displaystyle r(A)=\|A\|_{\operatorname {op} }} {\displaystyle r(A)=\|A\|_{\operatorname {op} }} if (but not only if) A {\displaystyle A} {\displaystyle A} is normal.
  4. r ( A n ) r ( A ) n {\displaystyle r(A^{n})\leq r(A)^{n}} {\displaystyle r(A^{n})\leq r(A)^{n}}.

Proofs

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Most of the claims are obvious. Some are not.

General properties

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Proof of (13)

If A {\textstyle A} {\textstyle A} is Hermitian, then it is normal, so it is the convex hull of its eigenvalues, which are all real.

Conversely, assume W ( A ) {\textstyle W(A)} {\textstyle W(A)} is on the real line. Decompose A = B + C {\textstyle A=B+C} {\textstyle A=B+C}, where B {\textstyle B} {\textstyle B} is a Hermitian matrix, and C {\textstyle C} {\textstyle C} an anti-Hermitian matrix. Since W ( C ) {\textstyle W(C)} {\textstyle W(C)} is on the imaginary line, if C 0 {\textstyle C\neq 0} {\textstyle C\neq 0}, then W ( A ) {\textstyle W(A)} {\textstyle W(A)} would stray from the real line. Thus C = 0 {\textstyle C=0} {\textstyle C=0}, and A {\textstyle A} {\textstyle A} is Hermitian.

The following proof is due to[5]

Proof of (12)

The elements of W ( A ) {\textstyle W(A)} {\textstyle W(A)} are of the form tr ( A P ) {\textstyle \operatorname {tr} (AP)} {\textstyle \operatorname {tr} (AP)}, where P {\textstyle P} {\textstyle P} is projection from C 2 {\textstyle \mathbb {C} ^{2}} {\textstyle \mathbb {C} ^{2}} to a one-dimensional subspace.

The space of all one-dimensional subspaces of C 2 {\textstyle \mathbb {C} ^{2}} {\textstyle \mathbb {C} ^{2}} is P C 1 {\textstyle \mathbb {P} \mathbb {C} ^{1}} {\textstyle \mathbb {P} \mathbb {C} ^{1}}, which is a 2-sphere. The image of a 2-sphere under a linear projection is a filled ellipse.

In more detail, such P {\textstyle P} {\textstyle P} are of the form 1 2 I + 1 2 [ cos 2 θ e i ϕ sin 2 θ e i ϕ sin 2 θ cos 2 θ ] = 1 2 [ 1 + z x + i y x i y 1 z ] {\displaystyle {\frac {1}{2}}I+{\frac {1}{2}}{\begin{bmatrix}\cos 2\theta &e^{i\phi }\sin 2\theta \\e^{-i\phi }\sin 2\theta &-\cos 2\theta \end{bmatrix}}={\frac {1}{2}}{\begin{bmatrix}1+z&x+iy\\x-iy&1-z\end{bmatrix}}} {\displaystyle {\frac {1}{2}}I+{\frac {1}{2}}{\begin{bmatrix}\cos 2\theta &e^{i\phi }\sin 2\theta \\e^{-i\phi }\sin 2\theta &-\cos 2\theta \end{bmatrix}}={\frac {1}{2}}{\begin{bmatrix}1+z&x+iy\\x-iy&1-z\end{bmatrix}}} where x , y , z {\textstyle x,y,z} {\textstyle x,y,z}, satisfying x 2 + y 2 + z 2 = 1 {\textstyle x^{2}+y^{2}+z^{2}=1} {\textstyle x^{2}+y^{2}+z^{2}=1}, is a point on the unit 2-sphere.

Therefore, the elements of W ( A ) {\textstyle W(A)} {\textstyle W(A)}, regarded as elements of R 2 {\textstyle \mathbb {R} ^{2}} {\textstyle \mathbb {R} ^{2}} is the composition of two real linear maps ( x , y , z ) 1 2 [ 1 + z x + i y x i y 1 z ] {\textstyle (x,y,z)\mapsto {\frac {1}{2}}{\begin{bmatrix}1+z&x+iy\\x-iy&1-z\end{bmatrix}}} {\textstyle (x,y,z)\mapsto {\frac {1}{2}}{\begin{bmatrix}1+z&x+iy\\x-iy&1-z\end{bmatrix}}} and M tr ( A M ) {\textstyle M\mapsto \operatorname {tr} (AM)} {\textstyle M\mapsto \operatorname {tr} (AM)}, which maps the 2-sphere to a filled ellipse.

Proof of (2)

W ( A ) {\textstyle W(A)} {\textstyle W(A)} is the image of a continuous map x x , A x {\textstyle x\mapsto \langle x,Ax\rangle } {\textstyle x\mapsto \langle x,Ax\rangle } from the P C n {\displaystyle \mathbb {PC} ^{n}} {\displaystyle \mathbb {PC} ^{n}}, so it is compact.

Given two complex nonzero vectors x , y {\textstyle x,y} {\textstyle x,y}, let P x , P y {\textstyle P_{x},P_{y}} {\textstyle P_{x},P_{y}} be their corresponding Hermitian projectors from C n {\textstyle \mathbb {C} ^{n}} {\textstyle \mathbb {C} ^{n}} to their respective spans. Let P {\textstyle P} {\textstyle P} be the Hermitian projector to the span of both. We have that P A P {\textstyle P^{*}AP} {\textstyle P^{*}AP} is an operator on Span ( x , y ) {\textstyle \operatorname {Span} (x,y)} {\textstyle \operatorname {Span} (x,y)}.

Therefore, the "restricted numerical range" of P A P {\textstyle P^{*}AP} {\textstyle P^{*}AP}, defined by { Tr ( P A P P z ) : z Span ( x , y ) , z 0 } {\textstyle \{\operatorname {Tr} (P^{*}APP_{z}):z\in \operatorname {Span} (x,y),z\neq 0\}} {\textstyle \{\operatorname {Tr} (P^{*}APP_{z}):z\in \operatorname {Span} (x,y),z\neq 0\}}, is a closed ellipse, according to (12). It is also the case that if z Span ( x , y ) {\textstyle z\in \operatorname {Span} (x,y)} {\textstyle z\in \operatorname {Span} (x,y)} is nonzero, then Tr ( P A P P z ) = Tr ( A P P z P ) = Tr ( A P z ) W ( A ) {\textstyle \operatorname {Tr} (P^{*}APP_{z})=\operatorname {Tr} (APP_{z}P)=\operatorname {Tr} (AP_{z})\in W(A)} {\textstyle \operatorname {Tr} (P^{*}APP_{z})=\operatorname {Tr} (APP_{z}P)=\operatorname {Tr} (AP_{z})\in W(A)}. Therefore, the restricted numerical range is contained in the full numerical range of A {\textstyle A} {\textstyle A}.

Thus, if W ( A ) {\textstyle W(A)} {\textstyle W(A)} contains Tr ( A P x ) , Tr ( A P y ) {\textstyle \operatorname {Tr} (AP_{x}),\operatorname {Tr} (AP_{y})} {\textstyle \operatorname {Tr} (AP_{x}),\operatorname {Tr} (AP_{y})}, then it contains a closed ellipse that also contains Tr ( A P x ) , Tr ( A P y ) {\textstyle \operatorname {Tr} (AP_{x}),\operatorname {Tr} (AP_{y})} {\textstyle \operatorname {Tr} (AP_{x}),\operatorname {Tr} (AP_{y})}, so it contains the line segment between them.

Proof of (5)

Let W {\textstyle W} {\textstyle W} satisfy these properties. Let W 0 {\textstyle W_{0}} {\textstyle W_{0}} be the original numerical range.

Fix some matrix A {\textstyle A} {\textstyle A}. We show that the supporting planes of W ( A ) {\textstyle W(A)} {\textstyle W(A)} and W 0 ( A ) {\textstyle W_{0}(A)} {\textstyle W_{0}(A)} are identical. This would then imply that W ( A ) = W 0 ( A ) {\textstyle W(A)=W_{0}(A)} {\textstyle W(A)=W_{0}(A)} since they are both convex and compact.

By property (4), W ( A ) {\textstyle W(A)} {\textstyle W(A)} is nonempty. Let z {\textstyle z} {\textstyle z} be a point on the boundary of W ( A ) {\textstyle W(A)} {\textstyle W(A)}, then we can translate and rotate the complex plane so that the point translates to the origin, and the region W ( A ) {\textstyle W(A)} {\textstyle W(A)} falls entirely within C + {\textstyle \mathbb {C} ^{+}} {\textstyle \mathbb {C} ^{+}}. That is, for some ϕ R {\textstyle \phi \in \mathbb {R} } {\textstyle \phi \in \mathbb {R} }, the set e i ϕ ( W ( A ) z ) {\textstyle e^{i\phi }(W(A)-z)} {\textstyle e^{i\phi }(W(A)-z)} lies entirely within C + {\textstyle \mathbb {C} ^{+}} {\textstyle \mathbb {C} ^{+}}, while for any t > 0 {\textstyle t>0} {\textstyle t>0}, the set e i ϕ ( W ( A ) z ) t I {\textstyle e^{i\phi }(W(A)-z)-tI} {\textstyle e^{i\phi }(W(A)-z)-tI} does not lie entirely in C + {\textstyle \mathbb {C} ^{+}} {\textstyle \mathbb {C} ^{+}}.

The two properties of W {\textstyle W} {\textstyle W} then imply that e i ϕ ( A z ) + e i ϕ ( A z ) 0 {\displaystyle e^{i\phi }(A-z)+e^{-i\phi }(A-z)^{*}\succeq 0} {\displaystyle e^{i\phi }(A-z)+e^{-i\phi }(A-z)^{*}\succeq 0} and that inequality is sharp, meaning that e i ϕ ( A z ) + e i ϕ ( A z ) {\textstyle e^{i\phi }(A-z)+e^{-i\phi }(A-z)^{*}} {\textstyle e^{i\phi }(A-z)+e^{-i\phi }(A-z)^{*}} has a zero eigenvalue. This is a complete characterization of the supporting planes of W ( A ) {\textstyle W(A)} {\textstyle W(A)}.

The same argument applies to W 0 ( A ) {\textstyle W_{0}(A)} {\textstyle W_{0}(A)}, so they have the same supporting planes.

Normal matrices

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Proof of (1), (2)

For (2), if A {\textstyle A} {\textstyle A} is normal, then it has a full eigenbasis, so it reduces to (1).

Since A {\textstyle A} {\textstyle A} is normal, by the spectral theorem, there exists a unitary matrix U {\textstyle U} {\textstyle U} such that A = U D U {\textstyle A=UDU^{*}} {\textstyle A=UDU^{*}}, where D {\textstyle D} {\textstyle D} is a diagonal matrix containing the eigenvalues λ 1 , λ 2 , , λ n {\textstyle \lambda _{1},\lambda _{2},\ldots ,\lambda _{n}} {\textstyle \lambda _{1},\lambda _{2},\ldots ,\lambda _{n}} of A {\textstyle A} {\textstyle A}.

Let x = c 1 v 1 + c 2 v 2 + + c k v k {\textstyle x=c_{1}v_{1}+c_{2}v_{2}+\cdots +c_{k}v_{k}} {\textstyle x=c_{1}v_{1}+c_{2}v_{2}+\cdots +c_{k}v_{k}}. Using the linearity of the inner product, that A v j = λ j v j {\textstyle Av_{j}=\lambda _{j}v_{j}} {\textstyle Av_{j}=\lambda _{j}v_{j}}, and that { v i } {\textstyle \left\{v_{i}\right\}} {\textstyle \left\{v_{i}\right\}} are orthonormal, we have:

x , A x = i , j = 1 k c i c j v i , λ j v j = i = 1 k | c i | 2 λ i hull ( λ 1 , , λ k ) {\displaystyle \langle x,Ax\rangle =\sum _{i,j=1}^{k}c_{i}^{*}c_{j}\left\langle v_{i},\lambda _{j}v_{j}\right\rangle =\sum _{i=1}^{k}\left|c_{i}\right|^{2}\lambda _{i}\in \operatorname {hull} \left(\lambda _{1},\ldots ,\lambda _{k}\right)} {\displaystyle \langle x,Ax\rangle =\sum _{i,j=1}^{k}c_{i}^{*}c_{j}\left\langle v_{i},\lambda _{j}v_{j}\right\rangle =\sum _{i=1}^{k}\left|c_{i}\right|^{2}\lambda _{i}\in \operatorname {hull} \left(\lambda _{1},\ldots ,\lambda _{k}\right)}

Proof (3)

By affineness of W {\textstyle W} {\textstyle W}, we can translate and rotate the complex plane, so that we reduce to the case where W ( A ) {\textstyle \partial W(A)} {\textstyle \partial W(A)} has a sharp point at 0 {\textstyle 0} {\textstyle 0}, and that the two supporting planes at that point both make an angle ϕ 1 , ϕ 2 {\textstyle \phi _{1},\phi _{2}} {\textstyle \phi _{1},\phi _{2}} with the imaginary axis, such that ϕ 1 < ϕ 2 , e i ϕ 1 e i ϕ 2 {\textstyle \phi _{1}<\phi _{2},e^{i\phi _{1}}\neq e^{i\phi _{2}}} {\textstyle \phi _{1}<\phi _{2},e^{i\phi _{1}}\neq e^{i\phi _{2}}} since the point is sharp.

Since 0 W ( A ) {\textstyle 0\in W(A)} {\textstyle 0\in W(A)}, there exists a unit vector x 0 {\textstyle x_{0}} {\textstyle x_{0}} such that x 0 A x 0 = 0 {\textstyle x_{0}^{*}Ax_{0}=0} {\textstyle x_{0}^{*}Ax_{0}=0}.

By general property (4), the numerical range lies in the sectors defined by: Re ( e i θ x , A x ) 0 for all  θ [ ϕ 1 , ϕ 2 ]  and nonzero  x C n . {\displaystyle \operatorname {Re} \left(e^{i\theta }\langle x,Ax\rangle \right)\geq 0\quad {\text{for all }}\theta \in [\phi _{1},\phi _{2}]{\text{ and nonzero }}x\in \mathbb {C} ^{n}.} {\displaystyle \operatorname {Re} \left(e^{i\theta }\langle x,Ax\rangle \right)\geq 0\quad {\text{for all }}\theta \in [\phi _{1},\phi _{2}]{\text{ and nonzero }}x\in \mathbb {C} ^{n}.} At x = x 0 {\textstyle x=x_{0}} {\textstyle x=x_{0}}, the directional derivative in any direction y {\textstyle y} {\textstyle y} must vanish to maintain non-negativity. Specifically:
d d t Re ( e i θ x 0 + t y , A ( x 0 + t y ) ) | t = 0 = 0 y C n , θ [ ϕ 1 , ϕ 2 ] . {\displaystyle \left.{\frac {d}{dt}}\operatorname {Re} \left(e^{i\theta }\langle x_{0}+ty,A(x_{0}+ty)\rangle \right)\right|_{t=0}=0\quad \forall y\in \mathbb {C} ^{n},\theta \in [\phi _{1},\phi _{2}].} {\displaystyle \left.{\frac {d}{dt}}\operatorname {Re} \left(e^{i\theta }\langle x_{0}+ty,A(x_{0}+ty)\rangle \right)\right|_{t=0}=0\quad \forall y\in \mathbb {C} ^{n},\theta \in [\phi _{1},\phi _{2}].} Expanding this derivative:
Re ( e i θ ( y , A x 0 + x 0 , A y ) ) = 0 y C n , θ [ ϕ 1 , ϕ 2 ] . {\displaystyle \operatorname {Re} \left(e^{i\theta }\left(\langle y,Ax_{0}\rangle +\langle x_{0},Ay\rangle \right)\right)=0\quad \forall y\in \mathbb {C} ^{n},\theta \in [\phi _{1},\phi _{2}].} {\displaystyle \operatorname {Re} \left(e^{i\theta }\left(\langle y,Ax_{0}\rangle +\langle x_{0},Ay\rangle \right)\right)=0\quad \forall y\in \mathbb {C} ^{n},\theta \in [\phi _{1},\phi _{2}].}

Since the above holds for all θ [ ϕ 1 , ϕ 2 ] {\textstyle \theta \in [\phi _{1},\phi _{2}]} {\textstyle \theta \in [\phi _{1},\phi _{2}]}, we must have: y , A x 0 + x 0 , A y = 0 y C n . {\displaystyle \langle y,Ax_{0}\rangle +\langle x_{0},Ay\rangle =0\quad \forall y\in \mathbb {C} ^{n}.} {\displaystyle \langle y,Ax_{0}\rangle +\langle x_{0},Ay\rangle =0\quad \forall y\in \mathbb {C} ^{n}.}

For any y C n {\textstyle y\in \mathbb {C} ^{n}} {\textstyle y\in \mathbb {C} ^{n}} and α C {\textstyle \alpha \in \mathbb {C} } {\textstyle \alpha \in \mathbb {C} }, substitute α y {\textstyle \alpha y} {\textstyle \alpha y} into the equation: α y , A x 0 + α x 0 , A y = 0. {\displaystyle \alpha \langle y,Ax_{0}\rangle +\alpha ^{*}\langle x_{0},Ay\rangle =0.} {\displaystyle \alpha \langle y,Ax_{0}\rangle +\alpha ^{*}\langle x_{0},Ay\rangle =0.} Choose α = 1 {\textstyle \alpha =1} {\textstyle \alpha =1} and α = i {\textstyle \alpha =i} {\textstyle \alpha =i}, then simplify, we obtain y , A x 0 = 0 {\displaystyle \langle y,Ax_{0}\rangle =0} {\displaystyle \langle y,Ax_{0}\rangle =0} for all y {\displaystyle y} {\displaystyle y}, thus A x 0 = 0 {\textstyle Ax_{0}=0} {\textstyle Ax_{0}=0}.

Numerical radius

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Proof of (2)

Let v = arg max x 2 = 1 | x , A x | {\textstyle v=\arg \max _{\|x\|_{2}=1}|\langle x,Ax\rangle |} {\textstyle v=\arg \max _{\|x\|_{2}=1}|\langle x,Ax\rangle |}. We have r ( A ) = | v , A v | {\textstyle r(A)=|\langle v,Av\rangle |} {\textstyle r(A)=|\langle v,Av\rangle |}.

By Cauchy–Schwarz, | v , A v | v 2 A v 2 = A v 2 A o p {\displaystyle |\langle v,Av\rangle |\leq \|v\|_{2}\|Av\|_{2}=\|Av\|_{2}\leq \|A\|_{op}} {\displaystyle |\langle v,Av\rangle |\leq \|v\|_{2}\|Av\|_{2}=\|Av\|_{2}\leq \|A\|_{op}}

For the other one, let A = B + i C {\textstyle A=B+iC} {\textstyle A=B+iC}, where B , C {\textstyle B,C} {\textstyle B,C} are Hermitian. A o p B o p + C o p {\displaystyle \|A\|_{op}\leq \|B\|_{op}+\|C\|_{op}} {\displaystyle \|A\|_{op}\leq \|B\|_{op}+\|C\|_{op}}

Since W ( B ) {\textstyle W(B)} {\textstyle W(B)} is on the real line, and W ( i C ) {\textstyle W(iC)} {\textstyle W(iC)} is on the imaginary line, the extremal points of W ( B ) , W ( i C ) {\textstyle W(B),W(iC)} {\textstyle W(B),W(iC)} appear in W ( A ) {\textstyle W(A)} {\textstyle W(A)}, shifted, thus both B o p = r ( B ) r ( A ) , C o p = r ( i C ) r ( A ) {\textstyle \|B\|_{op}=r(B)\leq r(A),\|C\|_{op}=r(iC)\leq r(A)} {\textstyle \|B\|_{op}=r(B)\leq r(A),\|C\|_{op}=r(iC)\leq r(A)}.

Generalisations

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Higher-rank numerical range

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The numerical range is equivalent to the following definition: W ( A ) = { λ C : P M P = λ P  for some Hermitian projector  P  of rank  1 } {\displaystyle W(A)=\{\lambda \in \mathbb {C} :PMP=\lambda P{\text{ for some Hermitian projector }}P{\text{ of rank }}1\}} {\displaystyle W(A)=\{\lambda \in \mathbb {C} :PMP=\lambda P{\text{ for some Hermitian projector }}P{\text{ of rank }}1\}}This allows a generalization to higher-rank numerical ranges, one for each k = 1 , 2 , 3 , {\displaystyle k=1,2,3,\dots } {\displaystyle k=1,2,3,\dots }:[6] W k ( A ) = { λ C : P M P = λ P  for some Hermitian projector  P  of rank  k } {\displaystyle W_{k}(A)=\{\lambda \in \mathbb {C} :PMP=\lambda P{\text{ for some Hermitian projector }}P{\text{ of rank }}k\}} {\displaystyle W_{k}(A)=\{\lambda \in \mathbb {C} :PMP=\lambda P{\text{ for some Hermitian projector }}P{\text{ of rank }}k\}} W k ( A ) {\displaystyle W_{k}(A)} {\displaystyle W_{k}(A)} is always closed and convex,[7] [8] but it might be empty. It is guaranteed to be nonempty if k < n / 3 + 1 {\displaystyle k<n/3+1} {\displaystyle k<n/3+1}, and there exists some A {\displaystyle A} {\displaystyle A} such that W k ( A ) {\displaystyle W_{k}(A)} {\displaystyle W_{k}(A)} is empty if k n / 3 + 1 {\displaystyle k\geq n/3+1} {\displaystyle k\geq n/3+1}.[9]

See also

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Bibliography

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Books

Papers

References

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  1. ^ ""well-known" inequality for numerical radius of an operator". StackExchange .
  2. ^ "Upper bound for norm of Hilbert space operator". StackExchange .
  3. ^ "Inequalities for numerical radius of complex Hilbert space operator". StackExchange .
  4. ^ Hilary Priestley. "B4b hilbert spaces: extended synopses 9. Spectral theory" (PDF). In fact, ‖T‖ = max(−mT , MT) = wT. This fails for non-self-adjoint operators, but wT ≤ ‖T‖ ≤ 2wT in the complex case.
  5. ^ Davis, Chandler (June 1971). "The Toeplitz-Hausdorff Theorem Explained". Canadian Mathematical Bulletin. 14 (2): 245–246. doi:10.4153/CMB-1971-042-7. ISSN 0008-4395.
  6. ^ Choi, Man-Duen; Kribs, David W.; Życzkowski, Karol (October 2006). "Higher-rank numerical ranges and compression problems" . Linear Algebra and its Applications. 418 (2–3): 828–839. doi:10.1016/j.laa.2006年03月01日9.
  7. ^ Li, Chi-Kwong; Sze, Nung-Sing (2008). "Canonical Forms, Higher Rank Numerical Ranges, Totally Isotropic Subspaces, and Matrix Equations". Proceedings of the American Mathematical Society. 136 (9): 3013–3023. ISSN 0002-9939.
  8. ^ Woerdeman, Hugo J. (2008年01月01日). "The higher rank numerical range is convex" . Linear and Multilinear Algebra. 56 (1–2): 65–67. doi:10.1080/03081080701352211. ISSN 0308-1087.
  9. ^ Li, Chi-Kwong; Poon, Yiu-Tung; Sze, Nung-Sing (2009年06月01日). "Condition for the higher rank numerical range to be non-empty". Linear and Multilinear Algebra. 57 (4): 365–368. arXiv:0706.1540 . doi:10.1080/03081080701786384. ISSN 0308-1087.
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