A framework for randomized time-splitting in linear-quadratic optimal control
نویسندگان
چکیده
Inspired by the successes of stochastic algorithms in training deep neural networks and simulation interacting particle systems, we propose analyze a framework for randomized time-splitting linear-quadratic optimal control. In our proposed framework, linear dynamics original problem is replaced dynamics. To obtain dynamics, system matrix split into simpler submatrices time interval interest subintervals. The then found selecting randomly one or more each subinterval. We show that minimal values cost functional, control obtained with method converge expectation to their analogues when grid refined. derived convergence rates are validated several numerical experiments. Our results also indicate can lead reduction computational large-scale dynamical systems.
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ژورنال
عنوان ژورنال: Numerische Mathematik
سال: 2022
ISSN: ['0945-3245', '0029-599X']
DOI: https://doi.org/10.1007/s00211-022-01290-3