Processing math: 33%

Linear first-order ODE system

Set

context A:RMatrix(n,R)
context b:RRn
definiendum yit
postulate y:Ck(R,Rn)
postulate y(t)=A(t) y(t)+b(t)

Theorems

There exists a matrix S(t,s) such that the solution of the equation above is of the form

y(t)=S(t,0) y0+t0 S(t,s) b(s) ds

We don't know S(t,s) in general, but

S(t,0)=lim
Two special cases
S(t,s)=\mathrm{exp}\left((t-s)\ A\right)

and so

y(t)=\mathrm{exp}\left(t A\right)\cdot\left(y_0+\int_0^t\ \mathrm{exp}\left(-s A\right)\ b(s)\ \mathrm ds\right)

We can in fact sketch how to deal with this equation in cases where A(t) is a more general operator. Dyson series: Say we at least know how to apply A(t). The iterative solution technique for the equation is y_{n+1}(t)=y(0)+\int_{0}^t A(t)\,y_n(t)\,\mathrm dt. Note that “f(x):=y(0)+\mathrm{int}x ” iterated with initial condition y(0) gives \left(\sum_{n=0}^\infty\mathrm{int}^n\right)y(0). Factors \frac{1}{n!} are introduces when time-ordering the integrand and the resulting series is hence mnemonically written as y(t)=\mathcal T\exp(\mathrm{int}_{t_0}^tA(t))y(0)

S(t,s)=\mathrm{e}^{I(t)-I(s)}\,\,\mathrm{with}\,\, I(s):=\int_0^s A(\tau)\ \mathrm d\tau

Reference

Wikipedia: Dyson series


Context

Square matrix

Subset of

ODE system