AN ADAPTIVE AND LEARNING APPROACH TO SAMPLING OPTIMIZATION
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This paper provides a methodology to rapidly and accurately explore the cost function of an optimization problem based on rejection sampling. Initial results show good precision and rapid convergence. This leads to numerous applications where a multi-minima problem must be optimized or solved, such as those seen in control problems, optimal orbit maneuvers, or constellation design. The approach will be shown to be adaptive and to cover mixed integer-real functions.
author list (cited authors)
Henderson, T. A., Mortari, D., Avendano, M., & Junkins, J. L.