Sequentially-Defined Compressed Modes via ADMM

Abstract

The eigenfunctions of the discrete Laplace-Beltrami operator have played an important role in many aspects of geometry processing. Given the success of sparse representation methods in areas such as compressive sensing it is reasonable to find a sparse analogue of LBO eigenfunctions. This has been done by Ozolinš et al for Euclidean spaces and Neumann et al for surfaces where the resulting analogues are called compressed modes. In this short report we show that the method of Alternating Direction Method of Multipliers can be used to efficiently calculate compressed modes and that this compares well with a recent method to calculate them with an Iteratively Reweighted Least Squares method.