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Motivated by optimal control problems with PDEs under uncertainty, we present algorithms for constrained nonlinear optimization problems that use low-rank tensors. They are applied to optimal control of PDEs with uncertain parameters and to parametrized variational inequalities of obstacle type. These methods are tailored to the usage of low-rank tensor arithmetics and allow us to solve huge scale optimization problems. 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