Second-order systems of linear differential equations
of the form
d2xdt2=Ax
come up all over the place in physics, economics and engineering.
Fortunately, these almost always occur with matrices that are
diagonalizable and have real eigenvalues. In this section, we don't
have to worry about power vectors or complex eigenvalues!
Less fortunately, the solutions to the associated
scalar differential equations
d2ydt2=λy
take different forms depending on whether λ is positive, negative,
or zero. The elliptic case of λ<0 and the special case
λ=0 are explained in
the first video. The hyperbolic case of λ>0 is explained in
the second video.
Once we understand scalar 2nd order differential
equations d2ydt2=λy, we can understand systemsd2xdt2=Ax. As with first-order systems, we:
Diagonalize A.
Define y=[x]B.
Write the equations in terms of the y coordinates. The result is
a bunch of decoupled equations of the form d2yjdt2=λjyj.
Solve the decoupled equations, as in the videos above, and
Go around the square, converting initial conditions for x
(namely x(0) and ˙x(0)) into initial conditions for y
(that is, y(0) and ˙y(0))
into y(t) into x(t).
This is worked out in detail, with a 2×2 example, in the
following video.