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  2. Nilpotent matrix - Wikipedia

    en.wikipedia.org/wiki/Nilpotent_matrix

    nilpotent matrix is always less than or equal to. n {\displaystyle n} For example, every. 2 × 2 {\displaystyle 2\times 2} nilpotent matrix squares to zero. The determinant and trace of a nilpotent matrix are always zero. Consequently, a nilpotent matrix cannot be invertible. The only nilpotent diagonalizable matrix is the zero matrix.

  3. Jordan–Chevalley decomposition - Wikipedia

    en.wikipedia.org/wiki/Jordan–Chevalley...

    In mathematics, specifically linear algebra, the Jordan–Chevalley decomposition, named after Camille Jordan and Claude Chevalley, expresses a linear operator in a unique way as the sum of two other linear operators which are simpler to understand. Specifically, one part is potentially diagonalisable and the other is nilpotent.

  4. Nilpotent operator - Wikipedia

    en.wikipedia.org/wiki/Nilpotent_operator

    The Volterra operator is the corresponding integral operator T on the Hilbert space L2 (0,1) given by. The operator T is not nilpotent: take f to be the function that is 1 everywhere and direct calculation shows that Tn f ≠ 0 (in the sense of L2) for all n. However, T is quasinilpotent. First notice that K is in L2 (X, m), therefore T is compact.

  5. Nilpotent - Wikipedia

    en.wikipedia.org/wiki/Nilpotent

    Nilpotent. In mathematics, an element of a ring is called nilpotent if there exists some positive integer , called the index (or sometimes the degree), such that . The term, along with its sister idempotent, was introduced by Benjamin Peirce in the context of his work on the classification of algebras. [1]

  6. Schur decomposition - Wikipedia

    en.wikipedia.org/wiki/Schur_decomposition

    Schur decomposition. In the mathematical discipline of linear algebra, the Schur decomposition or Schur triangulation, named after Issai Schur, is a matrix decomposition. It allows one to write an arbitrary complex square matrix as unitarily similar to an upper triangular matrix whose diagonal elements are the eigenvalues of the original matrix.

  7. Perron–Frobenius theorem - Wikipedia

    en.wikipedia.org/wiki/Perron–Frobenius_theorem

    Let = be an positive matrix: > for ,.Then the following statements hold. There is a positive real number r, called the Perron root or the Perron–Frobenius eigenvalue (also called the leading eigenvalue, principal eigenvalue or dominant eigenvalue), such that r is an eigenvalue of A and any other eigenvalue λ (possibly complex) in absolute value is strictly smaller than r, |λ| < r.

  8. Nilpotent orbit - Wikipedia

    en.wikipedia.org/wiki/Nilpotent_orbit

    Nilpotent matrices with complex entries form the main motivating case for the general theory, corresponding to the complex general linear group.From the Jordan normal form of matrices we know that each nilpotent matrix is conjugate to a unique matrix with Jordan blocks of sizes …, where is a partition of n.

  9. Min-max theorem - Wikipedia

    en.wikipedia.org/wiki/Min-max_theorem

    Min-max theorem. In linear algebra and functional analysis, the min-max theorem, or variational theorem, or Courant–Fischer–Weyl min-max principle, is a result that gives a variational characterization of eigenvalues of compact Hermitian operators on Hilbert spaces. It can be viewed as the starting point of many results of similar nature.