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The MOVE Fire

This is a personal recollection on the Move fire on May 13, 1985 Philadelphia police fired thousands of rounds at the MOVE house, city officials approved dropping an explosive device on the roof, the resulting fire was allowed to burn, 11 people—including five children—died, and 61 homes were destroyed. Philadelphia City Council later called it a “brutal attack carried out by the City of Philadelphia on its own citizens” and acknowledged that no individual faced criminal consequences for the bombing. One timeline correction worth preserving for the BHP record: the major previous MOVE-police confrontation was August 8, 1978, about seven years before the bombing, not a year or two earlier. Officer James Ramp was killed, other police and firefighters were wounded, nine MOVE members were later convicted, and television cameras recorded police beating Delbert Africa during his arrest. The 1985 MOVE Commission later specifically criticized city planners for failing to adequately use lessons from that 1978 confrontation. And that actually strengthens the point you’re making: 1985 did not happen without precedent or institutional memory. There had already been a deadly confrontation with MOVE, years of conflict, negotiations and police involvement before Osage Avenue.

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BLACK FACTS
The Truths They Never Taught You...

Ruler of the Mali Empire in the 14th century

Mansa Musa was the ruler of the Mali Empire in West Africa. Details recorded here should be sourced; unknown information is left blank.

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BHP gathered finds from its connected research sources. Showing the 4 strongest Black History matches.
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Wikipedia

Numerical range

In the mathematical field of linear algebra and convex analysis, the numerical range or field of values or Wertvorrat or Wertevorrat 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).

Equivalently, the elements of are of the form , where is a Hermitian projection operator from 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.

Properties

[edit]

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

[edit]

Most of the claims are obvious. Some are not.

General properties

[edit]
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.

The following proof is due to[5]

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 , so it is compact.

Given two complex nonzero vectors , let be their corresponding Hermitian projectors from to their respective spans. Let be the Hermitian projector to the span of both. We have that is an operator on .

Therefore, the “restricted numerical range” of , defined by , is a closed ellipse, according to (12). It is also the case that if is nonzero, then . Therefore, the restricted numerical range is contained in the full numerical range of .

Thus, if contains , then it contains a closed ellipse that also contains , so it contains the line segment between them.

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

[edit]
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, such that 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]
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

[edit]

Higher-rank numerical range

[edit]

The numerical range is equivalent to the following definition: This allows a generalization to higher-rank numerical ranges, one for each :[6] is always closed and convex,[7][8] but it might be empty. It is guaranteed to be nonempty if , and there exists some such that is empty if .[9]

See also

[edit]

Bibliography

[edit]

Books

  • 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. (1973), 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
  • Bhatia, Rajendra (1997). Matrix analysis. Graduate texts in mathematics. New York Berlin Heidelberg: Springer. ISBN 978-0-387-94846-1.
  • Gustafson, Karl E.; Rao, Duggirala K. M. (1997). Numerical Range: The Field of Values of Linear Operators and Matrices. Universitext. New York, NY: Springer. doi:10.1007/978-1-4613-8498-4. ISBN 978-0-387-94835-5. ISSN 0172-5939.

Papers

References

[edit]
  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). Archived from the original (PDF) on 2014-12-25. 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.2006.03.019.
  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. arXiv:0706.1536. doi:10.1090/S0002-9939-08-09536-1. ISSN 0002-9939. JSTOR 20535511.
  8. ^ Woerdeman, Hugo J. (2008-01-01). "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 (2009-06-01). "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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Wikipedia

Numerical range

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} 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.\left\{{\frac {\mathbf {x} ^{*}A\mathbf {x} }{\mathbf {x} ^{*}\mathbf {x} }}\;\right|\;\mathbf {x} \in \mathbb {C} ^{n},\ \mathbf {x} \neq 0\right\}={\big \{}\langle \mathbf {x} ,A\mathbf {x} \rangle \;{\big |}\;\mathbf {x} \in \mathbb {C} ^{n},\ \|\mathbf {x} \|_{2}=1{\big \}}} where x ∗ {\displaystyle \mathbf {x} ^{*}} denotes the conjugate transpose of the vector 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 ) {\displaystyle W(A)} are of the form tr ⁡ ( A P ) {\displaystyle \operatorname {tr} (AP)} , where P {\displaystyle P} is a Hermitian projection operator from C n {\displaystyle \mathbb {C} ^{n}} 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 {\big \{}|\lambda |:\lambda \in W(A){\big \}}=\sup _{\|x\|_{2}=1}{\big |}\langle \mathbf {x} ,A\mathbf {x} \rangle {\big |}.}

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Wikipedia

Product numerical range

Given a Hilbert space with a tensor product structure a product numerical range is defined as a numerical range with respect to the subset of product vectors. In some situations, especially in the context of quantum mechanics product numerical range is known as local numerical range

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Wikipedia

Workshop on Numerical Ranges and Numerical Radii

Workshop on Numerical Ranges and Numerical Radii (WONRA) is a biennial workshop series on numerical ranges and numerical radii which began in 1992.

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Wikipedia

LMS color space

LMS (long, medium, short), is a color space which represents the response of the three types of cone cells of the human eye, named for their responsivity (sensitivity) peaks at long, medium, and short wavelengths. The numerical range is generally not specified, except that the lower end is generally bounded by zero. It is common to use the LMS color space when performing chromatic adaptation (estimating the appearance of a sample under a different illuminant). It is also useful in the study of color blindness, when one or more cone types are defective.

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TOPIC OF THE DAY

Greenwood / Black Wall Street

Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.

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TRIVIA QUESTION OF THE DAY

Which heavyweight champion was known as the “Brown Bomber”?

Joe Louis.