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Computing the k th power of a matrix needs k – 1 times the time of a single matrix multiplication, if it is done with the trivial algorithm (repeated multiplication). As this may be very time consuming, one generally prefers using exponentiation by squaring, which requires less than 2 log 2 k matrix multiplications, and is therefore much more ...
In linear algebra, the Strassen algorithm, named after Volker Strassen, is an algorithm for matrix multiplication.It is faster than the standard matrix multiplication algorithm for large matrices, with a better asymptotic complexity, although the naive algorithm is often better for smaller matrices.
In theoretical computer science, the computational complexity of matrix multiplication dictates how quickly the operation of matrix multiplication can be performed. Matrix multiplication algorithms are a central subroutine in theoretical and numerical algorithms for numerical linear algebra and optimization, so finding the fastest algorithm for matrix multiplication is of major practical ...
One of the reasons for the importance of the matrix exponential is that it can be used to solve systems of linear ordinary differential equations.The solution of = (), =, where A is a constant matrix and y is a column vector, is given by =.
[5] [6] Closed form solutions can be computed for special cases such as symmetric matrices with all diagonal and off-diagonal elements equal [7] or Toeplitz matrices [8] and for the general case as well. [9] [10] In general, the inverse of a tridiagonal matrix is a semiseparable matrix and vice versa. [11]
Given a formal Laurent series = =, the corresponding Hankel operator is defined as [2]: [] [[]]. This takes a polynomial [] and sends it to the product , but discards all powers of with a non-negative exponent, so as to give an element in [[]], the formal power series with strictly negative exponents.
The state-transition matrix is used to find the solution to a general state-space representation of a linear system in the following form ˙ = () + (), =, where () are the states of the system, () is the input signal, () and () are matrix functions, and is the initial condition at .
A symmetric real n × n matrix is called positive semidefinite if for all (here denotes the transpose, changing a column vector x into a row vector). A square real matrix is positive semidefinite if and only if = for some matrix B.