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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 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

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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. for all square matrix and complex numbers and . Here is the identity matrix.
  4. is a subset of the closed right half-plane if and only if is positive semidefinite.
  5. The numerical range is the only function on the set of square matrices that satisfies (2), (3) and (4).
  6. for any unitary .
  7. .
  8. If is Hermitian, then is on the real line. If is anti-Hermitian, then is on the imaginary line.
  9. if and only if .
  10. (Sub-additive) .
  11. contains all the eigenvalues of .
  12. The numerical range of a matrix is a filled ellipse.
  13. is a real line segment if and only if is a Hermitian matrix with its smallest and the largest eigenvalues being and .

Normal matrices

  1. If is normal, and , where are eigenvectors of corresponding to , respectively, then .
  2. If is a normal matrix then is the convex hull of its eigenvalues.
  3. If is a sharp point on the boundary of , then is a normal eigenvalue of .

Numerical radius

  1. is a unitarily invariant norm on the space of matrices.
  2. , where denotes the operator norm.[1][2][3][4]
  3. if (but not only if) is normal.
  4. .

Proofs

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Most of the claims are obvious. Some are not.

General properties

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Proof of (13)

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.

Proof of (12)

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.

Proof of (2)

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

Proof of (5)

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

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Proof of (1), (2)

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:

Proof (3)

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

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Proof of (2)

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

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See also

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Bibliography

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  • 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

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