A splitting algorithm for stochastic partial differential equations driven by linear multiplicative noise
نویسندگان
چکیده
We study the convergence of a Douglas-Rachford type splitting algorithm for the infinite dimensional stochastic differential equation dX + A(t)(X)dt = X dW in (0, T ); X(0) = x, where A(t) : V → V ′ is a nonlinear, monotone, coercive and demicontinuous operator with sublinear growth and V is a real Hilbert space with the dual V ′. V is densely and continuously embedded in the Hilbert space H and W is an H-valued Wiener process. The general case of a maximal monotone operators A(t) : H → H is also investigated.
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