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  2. Hilbert matrix - Wikipedia

    en.wikipedia.org/wiki/Hilbert_matrix

    The Hilbert matrix is also totally positive (meaning that the determinant of every submatrix is positive). The Hilbert matrix is an example of a Hankel matrix. It is also a specific example of a Cauchy matrix. The determinant can be expressed in closed form, as a special case of the Cauchy determinant. The determinant of the n × n Hilbert ...

  3. Hilbert space - Wikipedia

    en.wikipedia.org/wiki/Hilbert_space

    The notion of an orthonormal basis from linear algebra generalizes over to the case of Hilbert spaces. [89] In a Hilbert space H, an orthonormal basis is a family {e k} k ∈ B of elements of H satisfying the conditions: Orthogonality: Every two different elements of B are orthogonal: e k, e j = 0 for all k, j ∈ B with k ≠ j.

  4. Normal operator - Wikipedia

    en.wikipedia.org/wiki/Normal_operator

    The class of normal operators is well understood. Examples of normal operators are unitary operators: N* = N −1; Hermitian operators (i.e., self-adjoint operators): N* = N; skew-Hermitian operators: N* = −N; positive operators: N = MM* for some M (so N is self-adjoint). A normal matrix is the matrix expression of a normal operator on the ...

  5. Spectral theory - Wikipedia

    en.wikipedia.org/wiki/Spectral_theory

    In mathematics, spectral theory is an inclusive term for theories extending the eigenvector and eigenvalue theory of a single square matrix to a much broader theory of the structure of operators in a variety of mathematical spaces. [1] It is a result of studies of linear algebra and the solutions of systems of linear equations and their ...

  6. Matrix mechanics - Wikipedia

    en.wikipedia.org/wiki/Matrix_mechanics

    Born, however, had learned matrix algebra from Rosanes, as already noted, but Born had also learned Hilbert's theory of integral equations and quadratic forms for an infinite number of variables as was apparent from a citation by Born of Hilbert's work Grundzüge einer allgemeinen Theorie der Linearen Integralgleichungen published in 1912. [16 ...

  7. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix into a rotation, followed by a rescaling followed by another rotation. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any ⁠ m × n {\displaystyle m\times n} ⁠ matrix.