SQP methods and their application to numerical optimal control
In recent years, general-purpose sequential quadratic programming
(SQP) methods have been developed that can reliably solve constrained
optimization problems with many hundreds of variables and constraints.
These methods require remarkably few evaluations of the problem
functions and can be shown to converge to a solution under very mild
conditions on the problem.
Some practical and theoretical aspects of applying general-purpose
SQP methods to optimal control problems are discussed, including the
influence of the problem discretization and the zero/nonzero structure
of the problem derivatives. We conclude with some recent approaches
that tailor the SQP method to the control problem.
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