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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.
This definition can be applied in particular to square matrices.The matrix = is nilpotent because =.See nilpotent matrix for more.. In the factor ring /, the equivalence class of 3 is nilpotent because 3 2 is congruent to 0 modulo 9.
In the finite-dimensional case, i.e. when T is a square matrix (Nilpotent matrix) with complex entries, σ(T) = {0} if and only if T is similar to a matrix whose only nonzero entries are on the superdiagonal [2] (this fact is used to prove the existence of Jordan canonical form). In turn this is equivalent to T n = 0 for some n. Therefore, for ...
Perron–Frobenius theorem. In matrix theory, the Perron–Frobenius theorem, proved by Oskar Perron (1907) and Georg Frobenius (1912), asserts that a real square matrix with positive entries has a unique eigenvalue of largest magnitude and that eigenvalue is real. The corresponding eigenvector can be chosen to have strictly positive components ...
An n×n matrix with n distinct nonzero eigenvalues has 2 n square roots. Such a matrix, A, has an eigendecomposition VDV−1 where V is the matrix whose columns are eigenvectors of A and D is the diagonal matrix whose diagonal elements are the corresponding n eigenvalues λi. Thus the square roots of A are given by VD1/2 V−1, where D1/2 is ...
Nilpotent matrix: A square matrix satisfying A q = 0 for some positive integer q. Equivalently, the only eigenvalue of A is 0. Normal matrix: A square matrix that commutes with its conjugate transpose: AA ∗ = A ∗ A: They are the matrices to which the spectral theorem applies. Orthogonal matrix: A matrix whose inverse is equal to its ...
Trace (linear algebra) In linear algebra, the trace of a square matrix A, denoted tr (A), [1] is defined to be the sum of elements on the main diagonal (from the upper left to the lower right) of A. The trace is only defined for a square matrix (n × n). In mathematical physics texts, if tr (A) = 0 then the matrix is said to be traceless.
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.