Numerical range
In the mathematical field of linear algebra and convex analysis, the numerical range or field of values of a complex matrix A is the set
where denotes the conjugate transpose of the vector . 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).
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.
Properties
[edit]Let sum of sets denote a sumset.
General properties
- The numerical range is the range of the Rayleigh quotient.
- (Hausdorff–Toeplitz theorem) The numerical range is convex and compact.
- for all square matrix and complex numbers and . Here is the identity matrix.
- is a subset of the closed right half-plane if and only if is positive semidefinite.
- The numerical range is the only function on the set of square matrices that satisfies (2), (3) and (4).
- for any unitary .
- .
- If is Hermitian, then is on the real line. If is anti-Hermitian, then is on the imaginary line.
- if and only if .
- (Sub-additive) .
- contains all the eigenvalues of .
- The numerical range of a matrix is a filled ellipse.
- is a real line segment if and only if is a Hermitian matrix with its smallest and the largest eigenvalues being and .
- If is normal, and , where are eigenvectors of corresponding to , respectively, then .
- If is a normal matrix then is the convex hull of its eigenvalues.
- If is a sharp point on the boundary of , then is a normal eigenvalue of .
Numerical radius
- is a unitarily invariant norm on the space of matrices.
- , where denotes the operator norm.[1][2][3][4]
- if (but not only if) is normal.
- .
Proofs
[edit]Most of the claims are obvious. Some are not.
General properties
[edit]If is Hermitian, then it is normal, so it is the convex hull of its eigenvalues, which are all real.
Conversely, assume is on the real line. Decompose , where is a Hermitian matrix, and an anti-Hermitian matrix. Since is on the imaginary line, if , then would stray from the real line. Thus , and is Hermitian.
The elements of are of the form , where is projection from to a one-dimensional subspace.
The space of all one-dimensional subspaces of is , which is a 2-sphere. The image of a 2-sphere under a linear projection is a filled ellipse.
In more detail, such are of the form where , satisfying , is a point on the unit 2-sphere.
Therefore, the elements of , regarded as elements of is the composition of two real linear maps and , which maps the 2-sphere to a filled ellipse.
is the image of a continuous map from the closed unit sphere, so it is compact.
For any of unit norm, project to the span of as . Then is a filled ellipse by the previous result, and so for any , let , we have
Let satisfy these properties. Let be the original numerical range.
Fix some matrix . We show that the supporting planes of and are identical. This would then imply that since they are both convex and compact.
By property (4), is nonempty. Let be a point on the boundary of , then we can translate and rotate the complex plane so that the point translates to the origin, and the region falls entirely within . That is, for some , the set lies entirely within , while for any , the set does not lie entirely in .
The two properties of then imply that and that inequality is sharp, meaning that has a zero eigenvalue. This is a complete characterization of the supporting planes of .
The same argument applies to , so they have the same supporting planes.
Normal matrices
[edit]For (2), if is normal, then it has a full eigenbasis, so it reduces to (1).
Since is normal, by the spectral theorem, there exists a unitary matrix such that , where is a diagonal matrix containing the eigenvalues of .
Let . Using the linearity of the inner product, that , and that are orthonormal, we have:
By affineness of , we can translate and rotate the complex plane, so that we reduce to the case where has a sharp point at , and that the two supporting planes at that point both make an angle with the imaginary axis, where . Note that cannot be , since the point is sharp.
Since , there exists a unit vector such that .
By general property (4), the numerical range lies in the sectors defined by: At , the directional derivative in any direction must vanish to maintain non-negativity. Specifically:
Expanding this derivative:
Since the above holds for all , we must have:
For any and , substitute into the equation: Choose and , then simplify, we obtain for all , thus .
Numerical radius
[edit]Let . We have .
By Cauchy–Schwarz,
For the other one, let , where are Hermitian.
Since is on the real line, and is on the imaginary line, the extremal points of appear in , shifted, thus both .
Generalisations
[edit]- C-numerical range
- Higher-rank numerical range
- Joint numerical range
- Product numerical range
- Polynomial numerical hull
See also
[edit]Bibliography
[edit]- Toeplitz, Otto (1918). "Das algebraische Analogon zu einem Satze von Fejér" (PDF). Mathematische Zeitschrift (in German). 2 (1–2): 187–197. doi:10.1007/BF01212904. ISSN 0025-5874.
- Hausdorff, Felix (1919). "Der Wertvorrat einer Bilinearform". Mathematische Zeitschrift (in German). 3 (1): 314–316. doi:10.1007/BF01292610. ISSN 0025-5874.
- Choi, M.D.; Kribs, D.W.; Życzkowski (2006), "Quantum error correcting codes from the compression formalism", Rep. Math. Phys., 58 (1): 77–91, arXiv:quant-ph/0511101, Bibcode:2006RpMP...58...77C, doi:10.1016/S0034-4877(06)80041-8, S2CID 119427312.
- Bhatia, Rajendra (1997). Matrix analysis. Graduate texts in mathematics. New York Berlin Heidelberg: Springer. ISBN 978-0-387-94846-1.
- Dirr, G.; Helmkel, U.; Kleinsteuber, M.; Schulte-Herbrüggen, Th. (2006), "A new type of C-numerical range arising in quantum computing", Proc. Appl. Math. Mech., 6: 711–712, doi:10.1002/pamm.200610336.
- Bonsall, F.F.; Duncan, J. (1971), Numerical Ranges of Operators on Normed Spaces and of Elements of Normed Algebras, Cambridge University Press, ISBN 978-0-521-07988-4.
- Bonsall, F.F.; Duncan, J. (1971), Numerical Ranges II, Cambridge University Press, ISBN 978-0-521-20227-5.
- Horn, Roger A.; Johnson, Charles R. (1991), Topics in Matrix Analysis, Cambridge University Press, Chapter 1, ISBN 978-0-521-46713-1.
- Horn, Roger A.; Johnson, Charles R. (1990), Matrix Analysis, Cambridge University Press, Ch. 5.7, ex. 21, ISBN 0-521-30586-1
- Li, C.K. (1996), "A simple proof of the elliptical range theorem", Proc. Am. Math. Soc., 124 (7): 1985, doi:10.1090/S0002-9939-96-03307-2.
- Keeler, Dennis S.; Rodman, Leiba; Spitkovsky, Ilya M. (1997), "The numerical range of 3 × 3 matrices", Linear Algebra and Its Applications, 252 (1–3): 115, doi:10.1016/0024-3795(95)00674-5.
- Johnson, Charles R. (1976). "Functional characterizations of the field of values and the convex hull of the spectrum" (PDF). Proceedings of the American Mathematical Society. 61 (2). American Mathematical Society (AMS): 201–204. doi:10.1090/s0002-9939-1976-0437555-3. ISSN 0002-9939.
References
[edit]- ^ ""well-known" inequality for numerical radius of an operator". StackExchange.
- ^ "Upper bound for norm of Hilbert space operator". StackExchange.
- ^ "Inequalities for numerical radius of complex Hilbert space operator". StackExchange.
- ^ 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.