In
linear algebra, the
trace of an
n-by-
n square matrix A is defined to be the sum of the elements on the main diagonal (the diagonal from the upper left to the lower right) of
A, i.e.
- tr(A) = A[1,1] + A[2,2] + ... + A[n,n].
If one imagines that the matrix A describes a water flow, in the sense that for every x in R^{n}, the vector Ax represents the velocity of the water at the location x, then the trace of A can be interpreted as follows: given any region U in R^{n}, the net flow of water out of U is given by tr(A)· vol(U), where vol(U) is the volume of U. See divergence.
The trace is used to define characters of group representations.
Properties
The trace is a linear map in the sense that
- tr(A + B) = tr(A) + tr(B) for all n-by-n matrices A and B
- tr(rA) = r tr(A) for all n-by-n matrices A and all scalars r.
A matrix and its transpose have the same trace:
- tr(A) = tr(A^{T}).
If A is an n×m matrix
and B is an m×n matrix, then
- tr(AB) = tr(BA).
Using this fact, we can deduce that the trace of a product of square matrices is equal to the trace of any
cyclic permutation[?] of the product, a fact known as the
cyclic property of the trace. For example, with three square matrices
A,
B, and
C,
- tr(ABC) = tr(CAB) = tr(BCA).
More generally, the same is true if the matrices are not assumed square, but are so shaped that all of these products exist.
If A and B are similar, i.e. if there exists an invertible matrix X such that A = X^{-1}BX, then by the cyclic property,
- tr(A) = tr(B).
Because of this, one may define the trace of a linear map
f :
V -> V (where
V is a finite-
dimensional vector space) by choosing a
basis for
V, describing
f as a matrix relative to this basis, and taking the trace of this square matrix. The result will not depend on the basis chosen, since different bases will give rise to similar matrices.
There exist matrices which have the same trace but are not similar.
If A is a square n-by-n matrix with real or complex entries and if λ_{1},...,λ_{n} are the (complex) eigenvalues of A (listed according to their algebraic multiplicities[?]), then
- tr(A) = ∑ λ_{i}.
This follows from the fact that
A is always similar to its
Jordan form[?], an upper triangular matrix having λ
_{1},...,λ
_{n} on the main diagonal.
From the connection between the trace and the eigenvalues, one can derive a connection between the trace function, the exponential function, and the determinant:
- det(exp(A)) = exp(tr(A)).
The trace also prominently appears in Jacobi's formula for the
derivative of the determinant (see under
determinant).
Inner Product
For an m-by-n matrix A with complex (or real) entries, we have
- tr(A^{*}A) ≥ 0
with equality only if
A = 0.
The assignment
- <A, B> = tr(A^{*}B)
yields an
inner product on the space of all complex (or real)
m-by-
n matrices.
If m=n then the norm induced by the above inner product is called the Frobenius norm[?] of a square matricx. Indeed it is simply the Euclidean norm[?] if the matrix is considered as a vector of length n^{2}.
Generalization
The concept of trace of a matrix is generalised to the
trace class of bounded
linear operators on
Hilbert spaces.
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