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Sparse programming is an important tool in robotics, for example in real-time sparse inverse kinematic control with a minimum number of active joints, or autonomous goal selection. However, current approaches are limited to real-time control without consideration of the underlying non-linear problem. This prevents the application to non-linear problems like inverse kinematic planning while the robot autonomously chooses from a set of potential goal positions. Instead, kinematic reachability approximations are used while the robot's whole body motion is considered separately. Furthermore, the sparse constraints are not prioritized for intuitive problem formulation. Lastly, the computational effort of the used standard solvers is cubically dependent on the number of constraints which is problematic in the presence of a large number of possible goals. In this work, we address sparse hierarchical non-linear programs with tools from hierarchical non-linear programming to gain a holistic understanding of the problem at hand. The resulting sequential sparse hierarchical quadratic programming solver scales linearly in the number of constraints and enables the formulation of sparse non-linear equality and inequality constraints on any priority level without feasibility requirements. This enables efficient robot sparse hierarchical inverse kinematic planning and control with autonomous goal selection from a high number of possible goal positions without any reachability approximations.