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A heuristic solution approach, based on simulated annealing (SA), is developed to solve the optimal problem.
It then reviews optimal problem transformation techniques and their practical application.
The planning problem is described as an optimal problem with constraints.
A solution of the optimal problem is then presented via a linear matrix inequality (LMI) formulation.
As the optimal dual controller is computationally intractable, approximations of the optimal problem are searched.
Second, optimal problem of drift reliability-based optimization design is formulated in a dimensionless way.
Third, solution step of optimal problem is constructed using conjugate gradient method.
First the problem is expressed as an optimal problem governed by variational forms on a fixed domain.
The online computational algorithm based on C/GMRES approach is adopted to solve this nonlinear receding optimal problem.
Moreover, for a class of nonlinear systems, constrained input-to-state stabilizability implies the solvability of an inverse optimal problem.
Due to the optimal problem being NP-hard, we further develop a practical strategy that compilers can use on distributed memory machines with HPF programs.
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CEO of Professional Science Editing for Scientists @ prosciediting.com