which is a simplification of the general indexing problem, where the provided Miller indices v q serve as discrete targets for the optimization. In addition, for a solution to be accepted, we would like the norm of all residuals ∥M q -v q ∥, q ∈ Q ⋆ to be less than the bound β on the maximum allowed absolute error. Note that finding the optimal basis M in (2) can be done in closed-from, and that from the obtained estimate, it is straightforward to check whether the acceptance condition ∥M qv q ∥ ≤ β, q ∈ Q ⋆ , is met. However, the subset of points Q ⋆ is unknown a priori and needs to be estimated as well. For this purpose, we propose a robust estimation method akin to Least Trimmed Squares (LTS) . Recall that in LTS, the goal is to find a subset of a given cardinality and regression coefficients that minimize the squared error. In our case, the cardinality of the set Q ⋆ is variable as it depends on the prescribed error tolerance β. It can thus be understood as the dual version of the LTS, and can be then solved as a sequence of LTS problems where the cardinality of the set gets monotonically updated. The process roughly consists of the following steps: