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In previous papers [17,18], we have described an algorithm for globally solving dynamic optimization problems with integral objective functions subject to ordinary differential equations. The method employs a branch-and-bound algorithm on a Euclidean space utilizing convex relaxations of the dynamic optimization problem. We show that convex relaxations for an integrand pointwise in time imply convex...
This paper develops a theory for the global solution of nonconvex optimization problems with parameter-embedded linear dynamic systems. A quite general problem formulation is introduced and a solution is shown to exists. A convexity theory for integrals is then developed to construct convex relaxations for utilization in a branch-and-bound framework to calculate a global minimum. Interval analysis...
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