# Matrix difference equation

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Matrix difference equation

A matrix difference equation is a difference equation in which the value of a vector (or sometimes, a matrix) of variables at one point in time is related to its own value at one or more previous points in time, using matrices. Occasionally, the time-varying entity may itself be a matrix instead of a vector. The order of the equation is the maximum time gap between any two indicated values of the variable vector. For example,

xt = Axt − 1 + Bxt − 2

is an example of a second-order matrix difference equation, in which x is an n × 1 vector of variables and A and B are n×n matrices. This equation is homogeneous because there is no vector constant term added to the end of the equation. The same equation might also be written as

xt + 2 = Axt + 1 + Bxt

or as

xn = Axn − 1 + Bxn − 2.

The most commonly encountered matrix difference equations are first-order.

## Non-homogeneous first-order matrix difference equations and the steady state

An example of a non-homogeneous first-order matrix difference equation is $x_t = Ax_{t-1} + b \,$

with additive constant vector b. The steady state of this system is a value x* of the vector x which, if reached, would not be deviated from subsequently. x* is found by setting xt = xt − 1 = x * in the difference equation and solving for x* to obtain $x^{*} = [I-A]^{-1}b \,$

where I is the n×n identity matrix, and where it is assumed that [IA] is invertible. Then the non-homogeneous equation can be rewritten in homogeneous form in terms of deviations from the steady state: $[x_t - x^{*}] = A[x_{t-1}-x^{*}]. \,$

## Stability of the first-order case

The first-order matrix difference equation [xt - x*] = A[xt-1-x*] is stable -- that is, xt converges asymptotically to the steady state x* -- if and only if all eigenvalues of the transition matrix A (whether real or complex) have an absolute value which is less than 1.

## Solution of the first-order case

Assume that the equation has been put in the homogeneous form yt = Ayt − 1. Then we can iterate and substitute repeatedly from the initial condition y0, which is the initial value of the vector y and which must be known in order to find the solution:

y1 = Ay0,
y2 = Ay1 = AAy0 = A2y0,
y3 = Ay2 = AA2y0 = A3y0,

and so forth. By induction, we obtain the solution in terms of t:

yt = Aty0 = PDtP − 1y0,

where P is an n × n matrix whose columns are the eigenvectors of A (assuming the eigenvalues are all distinct) and D is an n × n diagonal matrix whose diagonal elements are the eigenvalues of A. This solution motivates the above stability result: At shrinks to the zero matrix over time if and only if the eigenvalues of A are all less than unity in absolute value.

## Extracting the dynamics of a single scalar variable from a first-order matrix system

Starting from the n-dimensional system yt = Ayt − 1, we can extract the dynamics of one of the state variables, say y1. The above solution equation for yt shows that the solution for y1,t is in terms of the n eigenvalues of A. Therefore the equation describing the evolution of y1 by itself must have a solution involving those same eigenvalues. This description intuitively motivates the equation of evolution of y1, which is $y_{1,t} = a_1 y_{1,t-1} + a_2 y_{1,t-2} + \dots + a_n y_{1,t-n}$

where the parameters ai are from the characteristic equation of the matrix A: $\lambda^{n} - a_1 \lambda^{n-1} - a_2 \lambda^{n-2} - \dots - a_n \lambda^{0} = 0.$

Thus each individual scalar variable of an n-dimensional first-order linear system evolves according to a univariate nth degree difference equation, which has the same stability property (stable or unstable) as does the matrix difference equation.

## Solution and stability of higher-order cases

Matrix difference equations of higher order—that is, with a time lag longer than one period—can be solved, and their stability analyzed, by converting them into first-order form using a block matrix. For example, suppose we have the second-order equation

xt = Axt − 1 + Bxt − 2

with the variable vector x being n×1 and A and B being n×n. This can be stacked in the form $\begin{pmatrix}x_t \\ x_{t-1} \\ \end{pmatrix} = \begin{pmatrix} \text{A} & \text{B} \\ \text{I} & 0 \\ \end{pmatrix} \begin{pmatrix} x_{t-1} \\ x_{t-2} \end{pmatrix},$

where I is the n×n identity matrix and 0 is the n×n zero matrix. Then denoting the 2n×1 stacked vector of current and once-lagged variables as zt and the 2n×2n block matrix as L, we have as before the solution

zt = Ltz0.

Also as before, this stacked equation and thus the original second-order equation are stable if and only if all eigenvalues of the matrix L are smaller than unity in absolute value.

## Nonlinear matrix difference equations: Riccati equations

In linear-quadratic-Gaussian control, there arises a nonlinear matrix equation for the evolution backwards through time of a current-and-future-cost matrix, denoted below as H. This equation is called a discrete dynamic Riccati equation, and it arises when a variable vector evolving according to a linear matrix difference equation is to be controlled by manipulating an exogenous vector in order to optimize a quadratic cost function. This Riccati equation assumes the following form or a similar form: $H_{t-1} = K +A'H_tA - A'H_tC(C'H_tC+R)^{-1}C'H_tA, \,$

where H, K, and A are n×n, C is n×k, R is k×k, n is the number of elements in the vector to be controlled, and k is the number of elements in the control vector. The parameter matrices A and C are from the linear equation, and the parameter matrices K and R are from the quadratic cost function.

In general this equation cannot be solved analytically for Ht in terms of t ; rather, the sequence of values for Ht is found by iterating the Riccati equation. However, it was shown in  that this Riccati equation can be solved analytically if R is the zero matrix and n=k+1, by reducing it to a scalar rational difference equation; moreover, for any k and n if the transition matrix A is nonsingular then the Riccati equation can be solved analytically in terms of the eigenvalues of a matrix, although these may need to be found numerically.

In most contexts the evolution of H backwards through time is stable, meaning that H converges to a particular fixed matrix H* which may be irrational even if all the other matrices are rational.

A related Riccati equation is

Xt + 1 = − (E + BXt)(C + AXt) − 1

in which the matrices X, A, B, C, and E are all n×n. This equation can be solved explicitly. Suppose $X_t = N_tD_t^{-1}$, which certainly holds for t=0 with N0 = X0 and with D0 equal to the identity matrix. Then using this in the difference equation yields $X_{t+1}=-(E+BN_tD_t^{-1})D_tD_t^{-1}(C+AN_tD_t^{-1})^{-1}$ $=-(ED_t+BN_t)[(C+AN_tD_t^{-1})D_t]^{-1}$
= − (EDt + BNt)[CDt + ANt] − 1 $=N_{t+1}D_{t+1}^{-1},$

so by induction the form $X_t=N_tD_t^{-1}$ holds for all t. Then the evolution of N and D can be written as $\begin{pmatrix} N_{t+1} \\ D_{t+1} \end{pmatrix} = \begin{pmatrix} -B & -E \\ A & C \end{pmatrix} \begin{pmatrix} N_t \\ D_t \end{pmatrix} = J \begin{pmatrix}N_t \\ D_t \end{pmatrix}.$

Thus $\begin{pmatrix} N_t \\ D_t \end{pmatrix} = J^{t} \begin{pmatrix} N_0 \\ D_0 \end{pmatrix}.$

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