Schur complement

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In linear algebra and the theory of matrices, the Schur complement of a block matrix is defined as follows.

Suppose A, B, C, D are respectively p × p, p × q, q × p, and q × q matrices, and D is invertible. Let

so that M is a (p + q) × (p + q) matrix.

Then the Schur complement of the block D of the matrix M is the p × p matrix defined by

and, if A is invertible, the Schur complement of the block A of the matrix M is the q × q matrix defined by

In the case that A or D is singular, substituting a generalized inverse for the inverses on M/A and M/D yields the generalized Schur complement.

The Schur complement is named after Issai Schur who used it to prove Schur's lemma, although it had been used previously.[1] Emilie Virginia Haynsworth was the first to call it the Schur complement.[2] The Schur complement is a key tool in the fields of numerical analysis, statistics and matrix analysis.


The Schur complement arises as the result of performing a block Gaussian elimination by multiplying the matrix M from the right with a block lower triangular matrix

Here Ip denotes a p×p identity matrix. After multiplication with the matrix L the Schur complement appears in the upper p×p block. The product matrix is

This is analogous to an LDU decomposition. That is, we have shown that

and inverse of M thus may be expressed involving D−1 and the inverse of Schur's complement (if it exists) only as

Cf. matrix inversion lemma which illustrates relationships between the above and the equivalent derivation with the roles of A and D interchanged.


  • M is a positive-definite symmetric matrix if and only if D and the Schur complement of D are positive-definite.
  • M is a positive-definite symmetric matrix if and only if A and the Schur complement of A are positive-definite.
  • If p and q are both 1 (i.e., A, B, C and D are all scalars), we get the familiar formula for the inverse of a 2-by-2 matrix:
provided that AD − BC is non-zero.
  • In general, if A is invertible, then
whenever this inverse exists.
  • The determinant of M is also clearly seen to be given by
which generalizes the determinant formula for 2 × 2 matrices.
  • (Guttman rank additivity formula) The rank of M is given by
  • (Haynsworth inertia additivity formula) The inertia of the block matrix M is equal to the inertia of A plus the inertia of M/A.

Application to solving linear equations[edit]

The Schur complement arises naturally in solving a system of linear equations such as

where x, a are p-dimensional column vectors, y, b are q-dimensional column vectors, and A, B, C, D are as above. Multiplying the bottom equation by and then subtracting from the top equation one obtains

Thus if one can invert D as well as the Schur complement of D, one can solve for x, and then by using the equation one can solve for y. This reduces the problem of inverting a matrix to that of inverting a p × p matrix and a q × q matrix. In practice, one needs D to be well-conditioned in order for this algorithm to be numerically accurate.

In electrical engineering this is often referred to as node elimination or Kron reduction.

Applications to probability theory and statistics[edit]

Suppose the random column vectors X, Y live in Rn and Rm respectively, and the vector (X, Y) in Rn + m has a multivariate normal distribution whose covariance is the symmetric positive-definite matrix

where is the covariance matrix of X, is the covariance matrix of Y and is the covariance matrix between X and Y.

Then the conditional covariance of X given Y is the Schur complement of C in [3]:

If we take the matrix above to be, not a covariance of a random vector, but a sample covariance, then it may have a Wishart distribution. In that case, the Schur complement of C in also has a Wishart distribution.[citation needed]

Schur complement condition for positive definiteness and positive semi-definiteness[edit]

Let X be a symmetric matrix given by

Let X/A be the Schur complement of A in X; i.e.,

and X/C be the Schur complement of C in X; i.e.,


  • X is positive definite if and only if A and X/A are both positive definite:
  • X is positive definite if and only if C and X/C are both positive definite:
  • If A is positive definite, then X is positive semi-definite if and only if X/A is positive semi-definite:
  • If C is positive definite, then X is positive semi-definite if and only if X/C is positive semi-definite:

The first and third statements can be derived[4] by considering the minimizer of the quantity

as a function of v (for fixed u).

Furthermore, since

and similarly for positive semi-definite matrices, the second (respectively fourth) statement is immediate from the first (resp. third) statement.

There is also a sufficient and necessary condition for the positive semi-definiteness of X in terms of a generalized Schur complement.[1] Precisely,

  • and

where denotes the generalized inverse of .

See also[edit]


  1. ^ a b Zhang, Fuzhen (2005). The Schur Complement and Its Applications. Springer. doi:10.1007/b105056. ISBN 0-387-24271-6.
  2. ^ Haynsworth, E. V., "On the Schur Complement", Basel Mathematical Notes, #BNB 20, 17 pages, June 1968.
  3. ^ von Mises, Richard (1964). "Chapter VIII.9.3". Mathematical theory of probability and statistics. Academic Press. ISBN 978-1483255385.
  4. ^ Boyd, S. and Vandenberghe, L. (2004), "Convex Optimization", Cambridge University Press (Appendix A.5.5)