Carleman matrix

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In mathematics, a Carleman matrix is a matrix used to convert function composition into matrix multiplication. It is often used in iteration theory to find the continuous iteration of functions which cannot be iterated by pattern recognition alone. Other uses of Carleman matrices occur in the theory of probability generating functions, and Markov chains.

Definition[edit]

The Carleman matrix of a function f(x) is defined as:

M[f]_{jk} = \frac{1}{k!}\left[\frac{d^k}{dx^k} (f(x))^j \right]_{x=0} ~,

so as to satisfy the (Taylor series) equation:

(f(x))^j = \sum_{k=0}^{\infty} M[f]_{jk} x^k.

For instance, the computation of f(x) by

f(x) = \sum_{k=0}^{\infty} M[f]_{1,k} x^k. ~

simply amounts to the dot-product of row 1 of  M[f] with a column vector \left[1,x,x^2,x^3,...\right]^\tau.

The entries of M[f] in the next row give the 2nd power of f(x):

f(x)^2 = \sum_{k=0}^{\infty} M[f]_{2,k} x^k ~,

and also, in order to have the zero'th power of f(x) in M[f], we aadopt the row 0 containing zeros everywhere except the first position, such that

f(x)^0 = 1 = \sum_{k=0}^{\infty} M[f]_{0,k} x^k = 1+ \sum_{k=1}^{\infty} 0* x^k ~.

Thus, the dot product of M[f] with the column vector \left[1,x,x^2,...\right]^\tau yields the column vector \left[1,f(x),f(x)^2,...\right]^\tau

 M[f] * \left[ 1,x,x^2,x^3,...\right]^\tau = \left[ 1,f(x),(f(x))^2,(f(x))^3,...\right]^\tau.

Bell matrix[edit]

The Bell matrix of a function f(x) is defined as

B[f]_{jk} = \frac{1}{j!}\left[\frac{d^j}{dx^j} (f(x))^k \right]_{x=0} ~,

so as to satisfy the equation

(f(x))^k = \sum_{j=0}^{\infty} B[f]_{jk} x^j ~,

so it is the transpose of the above Carleman matrix.


Jabotinsky matrix[edit]

Eri Jabotinsky developed that concept of matrices 1947 for the purpose of representation of convolutions of polynomials. Several authors refer to the Bell matrices as "Jabotinsky matrix" since (D. Knuth 1992, W.D. Lang 2000), and possibly this shall grow to a more canonical name.

Generalization[edit]

A generalization of the Carleman matrix of a function can be defined around any point, such as:

M[f]_{x_0} = M_x[x - x_0]M[f]M_x[x + x_0]

or M[f]_{x_0} = M[g] where g(x) = f(x + x_0) - x_0. This allows the matrix power to be related as:

(M[f]_{x_0})^n = M_x[x - x_0]M[f]^nM_x[x + x_0]

Matrix properties[edit]

These matrices satisfy the fundamental relationships:

  • M[f \circ g] = M[f]M[g] ~,
  • B[f \circ g] = B[g]B[f] ~,

which makes the Carleman matrix M a (direct) representation of f(x), and the Bell matrix B an anti-representation of f(x). Here the term f \circ g denotes the composition of functions f(g(x)).

Other properties include:

Examples[edit]

The Carleman matrix of a constant is:

M[a] = \left(\begin{array}{cccc}
1&0&0& \cdots \\
a&0&0& \cdots \\
a^2&0&0& \cdots \\
\vdots&\vdots&\vdots&\ddots
\end{array}\right)

The Carleman matrix of the identity function is:

M_x[x] = \left(\begin{array}{cccc}
1&0&0& \cdots \\
0&1&0& \cdots \\
0&0&1& \cdots \\
\vdots&\vdots&\vdots&\ddots
\end{array}\right)

The Carleman matrix of a constant addition is:

M_x[a + x] = \left(\begin{array}{cccc}
1&0&0& \cdots \\
a&1&0& \cdots \\
a^2&2a&1& \cdots \\
\vdots&\vdots&\vdots&\ddots
\end{array}\right)

The Carleman matrix of a constant multiple is:

M_x[cx] = \left(\begin{array}{cccc}
1&0&0& \cdots \\
0&c&0& \cdots \\
0&0&c^2& \cdots \\
\vdots&\vdots&\vdots&\ddots
\end{array}\right)

The Carleman matrix of a linear function is:

M_x[a + cx] = \left(\begin{array}{cccc}
1&0&0& \cdots \\
a&c&0& \cdots \\
a^2&2ac&c^2& \cdots \\
\vdots&\vdots&\vdots&\ddots
\end{array}\right)

The Carleman matrix of a function f(x) = \sum_{k=1}^{\infty}f_k x^k is:

M[f] = \left(\begin{array}{cccc}
1&0&0& \cdots \\
0&f_1&f_2& \cdots \\
0&0&f_1^2& \cdots \\
\vdots&\vdots&\vdots&\ddots
\end{array}\right)

The Carleman matrix of a function f(x) = \sum_{k=0}^{\infty}f_k x^k is:

M[f] = \left(\begin{array}{cccc}
1&0&0& \cdots \\
f_0&f_1&f_2& \cdots \\
f_0^2&2f_0f_1&f_1^2& \cdots \\
\vdots&\vdots&\vdots&\ddots
\end{array}\right)

See also[edit]

References[edit]