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In this case, diagonalizable matrices are dense in the space of all matrices, which means any defective matrix can be deformed into a diagonalizable matrix by a small perturbation; and the Jordan–Chevalley decomposition states that any matrix is uniquely the sum of a diagonalizable matrix and a nilpotent matrix.
Only diagonalizable matrices can be factorized in this way. When the matrix being factorized is a normal or real symmetric matrix, the decomposition is called "spectral decomposition", derived from the spectral theorem.
In fact, a given n-by-n matrix A is similar to a diagonal matrix (meaning that there is a matrix X such that X −1 AX is diagonal) if and only if it has n linearly independent eigenvectors. Such matrices are said to be diagonalizable .
An n × n matrix A is diagonalizable if and only if the sum of the dimensions of the eigenspaces is n. Or, equivalently, if and only if A has n linearly independent eigenvectors. Not all matrices are diagonalizable; matrices that are not diagonalizable are called defective matrices. Consider the following matrix:
This is because any function of a non-defective matrix acts directly on each of its eigenvalues, and the conjugate transpose of its spectral decomposition is , where is the diagonal matrix of eigenvalues. Likewise, if two normal matrices commute and are therefore simultaneously diagonalizable, any operation between these matrices also acts on ...
Two diagonalizable matrices and commute (=) if they are simultaneously diagonalizable (that is, there exists an invertible matrix such that both and are diagonal). [ 4 ] : p. 64 The converse is also true; that is, if two diagonalizable matrices commute, they are simultaneously diagonalizable. [ 5 ]
If is diagonalizable it may be decomposed as = where is an orthogonal matrix =, and is a diagonal matrix of the eigenvalues of . In the special case that A {\displaystyle A} is real symmetric, then Q {\displaystyle Q} and Λ {\displaystyle \Lambda } are also real.
Others, such as matrix addition, scalar multiplication, matrix multiplication, and row operations involve operations on matrix entries and therefore require that matrix entries are numbers or belong to a field or a ring. [8] In this section, it is supposed that matrix entries belong to a fixed ring, which is typically a field of numbers.