Abstract
The paper proposes goal-oriented error estimation and mesh refinement for smooth convex optimal control problems with elliptic PDE constraints using the value of the reduced cost functional as quantity of interest. Error representation, hierarchical error estimators, and greedy-style error indicators are derived and compared with their counterparts when using the all-at-once cost functional as quantity of interest. The efficiency of the error estimator and generated meshes is demonstrated on numerical examples.
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