Abstract
An aggregation technique for constraints with values in Hilbert spaces is suggested. The technique allows us to replace the original optimization problem by a sequence of subproblems having scalar or finite-dimensional constraints. Applications to optimal control, games, and stochastic programming are discussed in detail.
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