Adaptive sampling-based approximation of the sign of multivariate real-valued functions

Nicolas Perrin (1, 2) , Olivier Stasse (1) , Florent Lamiraux (3) , Eiichi Yoshida (1)

1 CNRS-AIST JRL - Joint Robotics Laboratory
2 LAAS - Laboratoire d'analyse et d'architecture des systèmes
3 LAAS-GEPETTO - Équipe Mouvement des Systèmes Anthropomorphes

In this technical report, we present an algorithm which approximates the sign of multivariate real-valued functions through adaptive sampling, and demonstrate a convergence result. The algorithm was specifically designed for real-time robotics applications where quick evaluations are often needed.

Keywords: approximation algorithm; adaptive sampling; quadratic programming

Domains: Data Structures and Algorithms [cs.DS]

Nicolas Perrin, Olivier Stasse, Florent Lamiraux, Eiichi Yoshida. Adaptive sampling-based approximation of the sign of multivariate real-valued functions. 2010. ⟨hal-00544891⟩