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In mathematics, in the field of control theory, a Sylvester equation is a matrix equation of the form: [1] A X + X B = C . {\displaystyle AX+XB=C.} It is named after English mathematician James Joseph Sylvester .
In numerical linear algebra, the alternating-direction implicit (ADI) method is an iterative method used to solve Sylvester matrix equations.It is a popular method for solving the large matrix equations that arise in systems theory and control, [1] and can be formulated to construct solutions in a memory-efficient, factored form.
In particular, the discrete-time Lyapunov equation (also known as Stein equation) for is A X A H − X + Q = 0 {\displaystyle AXA^{H}-X+Q=0} where Q {\displaystyle Q} is a Hermitian matrix and A H {\displaystyle A^{H}} is the conjugate transpose of A {\displaystyle A} , while the continuous-time Lyapunov equation is
A discrete signal or discrete-time signal is a time series consisting of a sequence of quantities. Unlike a continuous-time signal, a discrete-time signal is not a function of a continuous argument; however, it may have been obtained by sampling from a continuous-time signal.
One type of iteration can be obtained in the discrete time case by using the dynamic Riccati equation that arises in the finite-horizon problem: in the latter type of problem each iteration of the value of the matrix is relevant for optimal choice at each period that is a finite distance in time from a final time period, and if it is iterated ...
Let {} be a random process, and be any point in time (may be an integer for a discrete-time process or a real number for a continuous-time process). Then X t {\\displaystyle X_{t}} is the value (or realization ) produced by a given run of the process at time t {\\displaystyle t} .
The state of a linear, time-invariant discrete-time system is assumed to satisfy (+) = + () = + where, at time , () is the plant's state; () is its inputs; and () is its outputs. These equations simply say that the plant's current outputs and its future state are both determined solely by its current states and the current inputs.
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 .