Polar decomposition

In mathematics, the polar decomposition of a square real or complex matrix is a factorization of the form , where is a unitary matrix, and is a positive semi-definite Hermitian matrix ( is an orthogonal matrix, and is a positive semi-definite symmetric matrix in the real case), both square and of the same size.[1]

If a real matrix is interpreted as a linear transformation of -dimensional space , the polar decomposition separates it into a rotation or reflection of and a scaling of the space along a set of orthogonal axes.

The polar decomposition of a square matrix always exists. If is invertible, the decomposition is unique, and the factor will be positive-definite. In that case, can be written uniquely in the form , where is unitary, and is the unique self-adjoint logarithm of the matrix .[2] This decomposition is useful in computing the fundamental group of (matrix) Lie groups.[3]

The polar decomposition can also be defined as , where is a symmetric positive-definite matrix with the same eigenvalues as but different eigenvectors.

The polar decomposition of a matrix can be seen as the matrix analog of the polar form of a complex number as , where is its absolute value (a non-negative real number), and is a complex number with unit norm (an element of the circle group).

The definition may be extended to rectangular matrices by requiring to be a semi-unitary matrix, and to be a positive-semidefinite Hermitian matrix. The decomposition always exists, and is always unique. The matrix is unique if and only if has full rank.[4]

  1. ^ Hall 2015, Section 2.5.
  2. ^ Hall 2015, Theorem 2.17.
  3. ^ Hall 2015, Section 13.3.
  4. ^ Cite error: The named reference higham1990 was invoked but never defined (see the help page).

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