New optimized Schwarz algorithms for one dimensional Schrödinger equation with general potential
For researchers solving the 1D Schrödinger equation numerically, this work provides faster and more scalable domain decomposition methods, though the improvement is incremental as it builds on existing Schwarz framework.
This paper develops new optimized Schwarz algorithms for the 1D Schrödinger equation with linear or nonlinear potential, achieving a direct (non-iterative) process for time-independent linear potential and a preconditioned algorithm for time-dependent or nonlinear potentials. The new algorithms are robust, scalable up to 256 subdomains, and significantly reduce computation time compared to the classical iterative method.
The aim of this paper is to develop new optimized Schwarz algorithms for the one dimensional Schr{ö}dinger equation with linear or nonlinear potential. After presenting the classical algorithm which is an iterative process, we propose a new algorithm for the Schr{ö}dinger equation with time-independent linear potential. Thanks to two main ingredients (constructing explicitly the interface problem and using a direct method on the interface problem), the new algorithm turns to be a direct process. Thus, it is free to choose the transmission condition. Concerning the case of time-dependent linear potential or nonlinear potential, we propose to use a pre-processed linear operator as preconditioner which leads to a preconditioned algorithm. Numerically , the convergence is also independent of the transmission condition. In addition, both of these new algorithms implemented in parallel cluster are robust, scalable up to 256 sub domains (MPI process) and take much less computation time than the classical one, especially for the nonlinear case.