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An efficient computational framework to solve nonlinear parabolic optimal control problems with random coefficients is presented. This framework allows us to investigate the influence of randomness or uncertainty of problem's parameters values on the control provided by the optimal control theory. The proposed framework combines space-time multigrid methods(More)
A theoretical and computational framework is presented to obtain accurate controls for fast quantum state transitions that are needed in a host of applications such as nano electronic devices and quantum computing. This method is based on a reduced Hessian Krylov-Newton scheme applied to a norm-preserving discrete model of a dipole quantum control problem.(More)
The influence of randomness or uncertainty of the input data on the control provided by the optimal control theory framework is investigated considering a class of nonlinear parabolic optimal control problems. The focus is on governing equations where diffusion and reaction parameters are represented by random fields. For the purpose of this investigation,(More)
a r t i c l e i n f o a b s t r a c t A computer package (QUCON) is presented aimed at the solution of dipole quantum optimal control problems. This MATLAB package is based on a recently developed computational strategy based on a globalized reduced Hessian Krylov–Newton scheme and a discretize-before-optimize approach. To discretize the governing(More)
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