Future extreme adaptive optics (ExAO) systems have been suggested with up to 10 5 sensors and actuators. We analyze the computational speed of iterative reconstruction algorithms for such large systems. We compare a total of 15 different scalable methods, including multigrid, preconditioned conjugate-gradient, and several new variants of these. Simulations on a 128ϫ 128 square sensor/actuator geometry using Taylor frozen-flow dynamics are carried out using both open-loop and closed-loop measurements, and algorithms are compared on a basis of the mean squared error and floating-point multiplications required. We also investigate the use of warm starting, where the most recent estimate is used to initialize the iterative scheme. In open-loop estimation or pseudo-open-loop control, warm starting provides a significant computational speedup; almost every algorithm tested converges in one iteration. In a standard closed-loop implementation, using a single iteration per time step, most algorithms give the minimum error even in cold start, and every algorithm gives the minimum error if warm started. The best algorithm is therefore the one with the smallest computational cost per iteration, not necessarily the one with the best quasi-static performance.
Lessard REF analyzed computational speed and the effect of warm-starting for different iterative methods with large systems, including the multigrid method, the preconditioned conjugate-gradient method, and several new variants of these methods.
Warm-started wavefront reconstruction for adaptive optics
Journal of the Optical Society of America. A, Optics, image science, and vision
Journal of the Optical Society of America. A, Optics, image science, and vision
Physics Medicine