Pfaffian
In mathematics, the determinant of a skew-symmetric matrix can always be written as the square of a polynomial in the matrix entries, a polynomial with integer coefficients that only depend on the size of the matrix. The value of this polynomial, when applied to the coefficients of a skew-symmetric matrix, is called the Pfaffian of that matrix. The term Pfaffian was introduced by who indirectly named them after Johann Friedrich Pfaff. The Pfaffian is nonvanishing only for 2n × 2n skew-symmetric matrices, in which case it is a polynomial of degree n.
Explicitly, for a skew-symmetric matrix A,
which was first proved by, a work based on earlier work on Pfaffian systems of ordinary differential equations by Jacobi.
The fact that the determinant of any skew symmetric matrix is the square of a polynomial can be shown by writing the matrix as a block matrix,
then using induction and examining the Schur complement, which is skew symmetric as well.
Examples
The Pfaffian of a 2n × 2n skew-symmetric tridiagonal matrix is given asFormal definition
Let A = be a 2n × 2n skew-symmetric matrix. The Pfaffian of A is explicitly defined by the formulawhere S2n is the symmetric group of order ! and sgn is the signature of σ.
One can make use of the skew-symmetry of A to avoid summing over all possible permutations. Let Π be the set of all partitions of into pairs without regard to order. There are !/ = !! such partitions. An element α ∈ Π can be written as
with ik < jk and. Let
be the corresponding permutation. Given a partition α as above, define
The Pfaffian of A is then given by
The Pfaffian of a n×n skew-symmetric matrix for n odd is defined to be zero, as the determinant of an odd skew-symmetric matrix is zero, since for a skew-symmetric matrix,
and for n odd, this implies.
Recursive definition
By convention, the Pfaffian of the 0×0 matrix is equal to one. The Pfaffian of a skew-symmetric 2n×2n matrix A with n>0 can be computed recursively aswhere index i can be selected arbitrarily, is the Heaviside step function, and denotes the matrix A with both the i-th and j-th rows and columns removed. Note how for the special choice this reduces to the simpler expression:
Alternative definitions
One can associate to any skew-symmetric 2n×2n matrix A = a bivectorwhere is the standard basis of R2n. The Pfaffian is then defined by the equation
here ωn denotes the wedge product of n copies of ω.
A non-zero generalisation of the Pfaffian to odd dimensional matrices is given in the work of de Bruijn on multiple integrals involving determinants. In particular for any m x m matrix A, we use the formal definition above but set. For m odd, one can then show that this is equal to the usual Pfaffian of an x dimensional skew symmetric matrix where we have added an th column consisting of m elements 1, an th row consisting of m elements -1, and the corner element is zero. The usual properties of Pfaffians, for example the relation to the determinant, then apply to this extended matrix.
Properties and identities
Pfaffians have the following properties, which are similar to those of determinants.- Multiplication of a row and a column by a constant is equivalent to multiplication of the Pfaffian by the same constant.
- Simultaneous interchange of two different rows and corresponding columns changes the sign of the Pfaffian.
- A multiple of a row and corresponding column added to another row and corresponding column does not change the value of the Pfaffian.
Miscellaneous
For a 2n × 2n skew-symmetric matrix AFor an arbitrary 2n × 2n matrix B,
Substituting in this equation B = Am, one gets for all integer m
Derivative identities
If A depends on some variable xi, then the gradient of a Pfaffian is given byand the Hessian of a Pfaffian is given by
Trace identities
The product of the Pfaffians of skew-symmetric matrices A and B under the condition that ATB is a positive-definite matrix can be represented in the form of an exponentialSuppose A and B are 2n × 2n skew-symmetric matrices, then
and Bn are Bell polynomials.
Block matrices
For a block-diagonal matrixFor an arbitrary n × n matrix M:
It is often required to compute the pfaffian of a skew-symmetric matrix with the block structure
where and are skew-symmetric matrices and is a general rectangular matrix.
When is invertible, one has
This can be seen from Aitken block-diagonalization formula,
This decomposition involves a congruence transformations that allow to use the pfaffian property.
Similarly, when is invertible, one has
as can be seen by employing the decomposition
Calculating the Pfaffian numerically
Suppose A is a 2n × 2n skew-symmetric matrices, thenwhere is the second Pauli matrix, is an identity matrix of dimension n and we took the trace over a matrix logarithm.
This equality is based on the trace identity
and on the observation that.
Since calculating the Logarithm of a matrix is a computationally demanding task, one can instead compute all eigenvalues of, take the log of all of these and sum them up. This procedure merely exploits the property. This can be implemented in Mathematica within a single line:
Pf := Module, IdentityMatrix ]
For other efficient algorithms see.
Applications
- There exist programs for the numerical computation of the Pfaffian on various platforms .
- The Pfaffian is an invariant polynomial of a skew-symmetric matrix under a proper orthogonal change of basis. As such, it is important in the theory of characteristic classes. In particular, it can be used to define the Euler class of a Riemannian manifold which is used in the generalized Gauss–Bonnet theorem.
- The number of perfect matchings in a planar graph is given by a Pfaffian, hence is polynomial time computable via the FKT algorithm. This is surprising given that for general graphs, the problem is very difficult. This result is used to calculate the number of domino tilings of a rectangle, the partition function of Ising models in physics, or of Markov random fields in machine learning, where the underlying graph is planar. It is also used to derive efficient algorithms for some otherwise seemingly intractable problems, including the efficient simulation of certain types of restricted quantum computation. Read Holographic algorithm for more information.