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In mathematics, particularly in linear algebra, a **skew-symmetric** (or **antisymmetric** or **antimetric**^{[1]}) **matrix** is a square matrix whose transpose equals its negative. That is, it satisfies the condition^{[2]}^{: p. 38 }

In terms of the entries of the matrix, if denotes the entry in the -th row and -th column, then the skew-symmetric condition is equivalent to

The matrix

is skew-symmetric because

Throughout, we assume that all matrix entries belong to a field whose characteristic is not equal to 2. That is, we assume that 1 + 1 ≠ 0, where 1 denotes the multiplicative identity and 0 the additive identity of the given field. If the characteristic of the field is 2, then a skew-symmetric matrix is the same thing as a symmetric matrix.

- The sum of two skew-symmetric matrices is skew-symmetric.
- A scalar multiple of a skew-symmetric matrix is skew-symmetric.
- The elements on the diagonal of a skew-symmetric matrix are zero, and therefore its trace equals zero.
- If is a real skew-symmetric matrix and is a real eigenvalue, then , i.e. the nonzero eigenvalues of a skew-symmetric matrix are non-real.
- If is a real skew-symmetric matrix, then is invertible, where is the identity matrix.
- If is a skew-symmetric matrix then is a symmetric negative semi-definite matrix.

As a result of the first two properties above, the set of all skew-symmetric matrices of a fixed size forms a vector space. The space of skew-symmetric matrices has dimension

Let denote the space of matrices. A skew-symmetric matrix is determined by scalars (the number of entries above the main diagonal); a symmetric matrix is determined by scalars (the number of entries on or above the main diagonal). Let denote the space of skew-symmetric matrices and denote the space of symmetric matrices. If then

Notice that and This is true for every square matrix with entries from any field whose characteristic is different from 2. Then, since and

Denote by the standard inner product on The real matrix is skew-symmetric if and only if

This is also equivalent to for all (one implication being obvious, the other a plain consequence of for all and ).

Since this definition is independent of the choice of basis, skew-symmetry is a property that depends only on the linear operator and a choice of inner product.

skew symmetric matrices can be used to represent cross products as matrix multiplications.

Furthermore, if is a skew-symmetric (or skew-Hermitian) matrix, then for all .

Let be a skew-symmetric matrix. The determinant of satisfies

In particular, if is odd, and since the underlying field is not of characteristic 2, the determinant vanishes. Hence, all odd dimension skew symmetric matrices are singular as their determinants are always zero. This result is called **Jacobi’s theorem**, after Carl Gustav Jacobi (Eves, 1980).

The even-dimensional case is more interesting. It turns out that the determinant of for even can be written as the square of a polynomial in the entries of , which was first proved by Cayley:^{[3]}

This polynomial is called the *Pfaffian* of and is denoted . Thus the determinant of a real skew-symmetric matrix is always non-negative. However this last fact can be proved in an elementary way as follows: the eigenvalues of a real skew-symmetric matrix are purely imaginary (see below) and to every eigenvalue there corresponds the conjugate eigenvalue with the same multiplicity; therefore, as the determinant is the product of the eigenvalues, each one repeated according to its multiplicity, it follows at once that the determinant, if it is not 0, is a positive real number.

The number of distinct terms in the expansion of the determinant of a skew-symmetric matrix of order has been considered already by Cayley, Sylvester, and Pfaff. Due to cancellations, this number is quite small as compared the number of terms of a generic matrix of order , which is . The sequence (sequence A002370 in the OEIS) is

- 1, 0, 1, 0, 6, 0, 120, 0, 5250, 0, 395010, 0, …

and it is encoded in the exponential generating function

The latter yields to the asymptotics (for even)

The number of positive and negative terms are approximatively a half of the total, although their difference takes larger and larger positive and negative values as increases (sequence A167029 in the OEIS).

Three-by-three skew-symmetric matrices can be used to represent cross products as matrix multiplications. Consider vectors and Then, defining the matrix

the cross product can be written as

This can be immediately verified by computing both sides of the previous equation and comparing each corresponding element of the results.

One actually has

i.e., the commutator of skew-symmetric three-by-three matrices can be identified with the cross-product of three-vectors. Since the skew-symmetric three-by-three matrices are the Lie algebra of the rotation group this elucidates the relation between three-space , the cross product and three-dimensional rotations. More on infinitesimal rotations can be found below.

Since a matrix is similar to its own transpose, they must have the same eigenvalues. It follows that the eigenvalues of a skew-symmetric matrix always come in pairs ±λ (except in the odd-dimensional case where there is an additional unpaired 0 eigenvalue). From the spectral theorem, for a real skew-symmetric matrix the nonzero eigenvalues are all pure imaginary and thus are of the form where each of the are real.

Real skew-symmetric matrices are normal matrices (they commute with their adjoints) and are thus subject to the spectral theorem, which states that any real skew-symmetric matrix can be diagonalized by a unitary matrix. Since the eigenvalues of a real skew-symmetric matrix are imaginary, it is not possible to diagonalize one by a real matrix. However, it is possible to bring every skew-symmetric matrix to a block diagonal form by a special orthogonal transformation.^{[4]}^{[5]} Specifically, every real skew-symmetric matrix can be written in the form where is orthogonal and

for real positive-definite . The nonzero eigenvalues of this matrix are ±λ_{k} *i*. In the odd-dimensional case Σ always has at least one row and column of zeros.

More generally, every complex skew-symmetric matrix can be written in the form where is unitary and has the block-diagonal form given above with still real positive-definite. This is an example of the Youla decomposition of a complex square matrix.^{[6]}

A **skew-symmetric form** on a vector space over a field of arbitrary characteristic is defined to be a bilinear form

such that for all in

This defines a form with desirable properties for vector spaces over fields of characteristic not equal to 2, but in a vector space over a field of characteristic 2, the definition is equivalent to that of a symmetric form, as every element is its own additive inverse.

Where the vector space is over a field of arbitrary characteristic including characteristic 2, we may define an **alternating form** as a bilinear form such that for all vectors in

This is equivalent to a skew-symmetric form when the field is not of characteristic 2, as seen from

whence

A bilinear form will be represented by a matrix such that , once a basis of is chosen, and conversely an matrix on gives rise to a form sending to For each of symmetric, skew-symmetric and alternating forms, the representing matrices are symmetric, skew-symmetric and alternating respectively.

Skew-symmetric matrices over the field of real numbers form the tangent space to the real orthogonal group at the identity matrix; formally, the special orthogonal Lie algebra. In this sense, then, skew-symmetric matrices can be thought of as *infinitesimal rotations*.

Another way of saying this is that the space of skew-symmetric matrices forms the Lie algebra of the Lie group The Lie bracket on this space is given by the commutator:

It is easy to check that the commutator of two skew-symmetric matrices is again skew-symmetric:

The matrix exponential of a skew-symmetric matrix is then an orthogonal matrix :

The image of the exponential map of a Lie algebra always lies in the connected component of the Lie group that contains the identity element. In the case of the Lie group this connected component is the special orthogonal group consisting of all orthogonal matrices with determinant 1. So will have determinant +1. Moreover, since the exponential map of a connected compact Lie group is always surjective, it turns out that *every* orthogonal matrix with unit determinant can be written as the exponential of some skew-symmetric matrix. In the particular important case of dimension the exponential representation for an orthogonal matrix reduces to the well-known polar form of a complex number of unit modulus. Indeed, if a special orthogonal matrix has the form

with . Therefore, putting and it can be written

which corresponds exactly to the polar form of a complex number of unit modulus.

The exponential representation of an orthogonal matrix of order can also be obtained starting from the fact that in dimension any special orthogonal matrix can be written as where is orthogonal and S is a block diagonal matrix with blocks of order 2, plus one of order 1 if is odd; since each single block of order 2 is also an orthogonal matrix, it admits an exponential form. Correspondingly, the matrixMore intrinsically (i.e., without using coordinates), skew-symmetric linear transformations on a vector space with an inner product may be defined as the bivectors on the space, which are sums of simple bivectors (2-blades) The correspondence is given by the map where is the covector dual to the vector ; in orthonormal coordinates these are exactly the elementary skew-symmetric matrices. This characterization is used in interpreting the curl of a vector field (naturally a 2-vector) as an infinitesimal rotation or "curl", hence the name.

An matrix is said to be **skew-symmetrizable** if there exists an invertible diagonal matrix such that is skew-symmetric. For **real** matrices, sometimes the condition for to have positive entries is added.^{[7]}

**^**Richard A. Reyment; K. G. Jöreskog; Leslie F. Marcus (1996).*Applied Factor Analysis in the Natural Sciences*. Cambridge University Press. p. 68. ISBN 0-521-57556-7.**^**Lipschutz, Seymour; Lipson, Marc (September 2005).*Schaum's Outline of Theory and Problems of Linear Algebra*. McGraw-Hill. ISBN 9780070605022.**^**Cayley, Arthur (1847). "Sur les determinants gauches" [On skew determinants].*Crelle's Journal*.**38**: 93–96. Reprinted in Cayley, A. (2009). "Sur les Déterminants Gauches".*The Collected Mathematical Papers*. Vol. 1. pp. 410–413. doi:10.1017/CBO9780511703676.070. ISBN 978-0-511-70367-6.**^**Voronov, Theodore.*Pfaffian*, in: Concise Encyclopedia of Supersymmetry and Noncommutative Structures in Mathematics and Physics, Eds. S. Duplij, W. Siegel, J. Bagger (Berlin, New York: Springer 2005), p. 298.**^**Zumino, Bruno (1962). "Normal Forms of Complex Matrices".*Journal of Mathematical Physics*.**3**(5): 1055–1057. Bibcode:1962JMP.....3.1055Z. doi:10.1063/1.1724294.**^**Youla, D. C. (1961). "A normal form for a matrix under the unitary congruence group".*Can. J. Math*.**13**: 694–704. doi:10.4153/CJM-1961-059-8.**^**Fomin, Sergey; Zelevinsky, Andrei (2001). "Cluster algebras I: Foundations". arXiv:math/0104151v1.

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