2019/05/27 by K.S. Kumar, K S Sesh Kumar, Kumar, K S Sesh +4 · 1 citation
Computer Science · Engineering · Mathematics · #Advanced Graph Theory Research #Complexity and Algorithms in Graphs #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1905.11327
arxiv created 2019/05/27 · openalex publication_date 2019/05/27 · arxiv updated 2019/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of minimizing the sum of submodular set functions assuming minimization oracles of each summand function. Most existing approaches reformulate the problem as the convex minimization of the sum of the corresponding Lovász extensions and the squared Euclidean norm, leading to algorithms requiring total variation oracles of the summand functions; without further assumptions, these more complex oracles require many calls to the simpler minimization oracles often available in practice. In this paper, we consider a modified convex problem requiring constrained version of the total variation oracles that can be solved with significantly fewer calls to the simple minimization oracles. We support our claims by showing results on graph cuts for 2D and 3D graphs