Civil Rights
Movements, leaders, victories and the continuing fight for equality.
Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
Mansa Musa was the ruler of the Mali Empire in West Africa. Details recorded here should be sourced; unknown information is left blank.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
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.
Let sum of sets denote a sumset.
General properties
Numerical radius
Most of the claims are obvious. Some are not.
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]
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 , 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.
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.
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, 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 .
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 .
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]
Books
Papers
In fact, ‖T‖ = max(−mT , MT) = wT. This fails for non-self-adjoint operators, but wT ≤ ‖T‖ ≤ 2wT in the complex case.
Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
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 |}.}
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
Workshop on Numerical Ranges and Numerical Radii (WONRA) is a biennial workshop series on numerical ranges and numerical radii which began in 1992.
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.
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.
MORE →Joe Louis.