Multiderivative multistep method

A multiderivative multistep method uses higher time derivatives as well as values and first derivatives from several time levels. For an autonomous ordinary differential equation $y^{\prime }=f(y)$, the second time derivative is $y^{\prime \prime }=f^{\prime }(y)f(y)$ by the chain rule. Its local truncation error must include the higher-derivative terms. Consequently the usual second-order barrier for A-stable linear multistep methods, which only use first derivatives, does not apply.

For a fixed-step multiderivative multistep method, the ordinary zero-stability root condition still controls propagation of the starting errors when all derivative evaluation maps are uniformly Lipschitz continuous on the relevant bounded region. A local defect $O(h^{p+1})$ then yields global error $O(h^p)$ over a fixed time interval, provided the starting errors are $O(h^p)$ and the implicit updates use the nearby solution branch.

For example, writing the numerical error equation as $\rho (E)e_n=h{F}_h(e_{n+s})+d_n$, with $F_h$ uniformly Lipschitz continuous, a bounded impulse response for the root condition for a multistep method givesAbsorbing the last current-step term for small $h$ and applying the discrete Gronwall inequality gives the stated order. The higher derivatives enter through $F_h$, whose bound stays uniform as $h\to 0$.