Some applications of weighted norm inequalities to the error analysis of PDE-constrained optimization problems

The purpose of this work is to illustrate how the theory of Muckenhoupt weights, Muckenhoupt-weighted Sobolev spaces and the corresponding weighted norm inequalities can be used in the analysis and discretization of partial-differential-equation-constrained optimization problems. We consider a linear quadratic constrained optimization problem where the state solves a nonuniformly elliptic equation, a problem where the cost involves pointwise observations of the state and one where the state has singular sources, e.g., point masses. For all the three examples, we propose and analyse numerical schemes and provide error estimates in two and three dimensions. While some of these problems might have been considered before in the literature, our approach allows for a simpler, Hilbert-space-based analysis and discretization and further generalizations.