Infinitesimal generator (stochastic processes)

For a Markov process with transition semigroup $(P_t)$, its infinitesimal generator is the operatoron the functions for which this limit exists.

For a Markov process with infinitesimal generator $L$ and a suitable test function $f$, the displayed process is a local martingale. With integrability it is a true martingale and yields the corresponding expectation identity at admissible stopping times. For bounded $f,Lf$ in the semigroup generator domain, $P_{t}f-f={\int }_0^t{P}_{s}Lfds$. The Markov property applies this identity conditionally over each interval, proving the martingale increments have zero conditional expectation. Localization gives the local version. For a continuous diffusion, these identities are the martingale problem for $L$.

For the one-dimensional Itô equation $dX=b(X)dt+\sigma (X)dW$, the infinitesimal generator on smooth test functions is $L=b{\partial }_x+{\sigma }^2{\partial }_{xx}/2$. The Kolmogorov backward equation is $u_t=Lu$, while its formal adjoint produces the Fokker-Planck equation.

For a diffusion process with diffusion generator $L$, the Itô formula makes $\phi (X_t)-\phi (X_0)-{\int }_0^{t}L\phi (X_s)ds$ a local martingale. Under stopping and integrability conditions making it a true martingale, taking expectations gives the displayed identity. For Brownian motion, $L=\frac{1}{2}\Delta$, recovering the usual Brownian formula.