swMATH ID: 8759
Software Authors: Krause, Andreas
Description: SFO: a toolbox for submodular function optimization. In recent years, a fundamental problem structure has emerged as very useful in a variety of machine learning applications: Submodularity is an intuitive diminishing returns property, stating that adding an element to a smaller set helps more than adding it to a larger set. Similarly to convexity, submodularity allows one to efficiently find provably (near-) optimal solutions for large problems. We present SFO, a toolbox for use in MATLAB or Octave that implements algorithms for minimization and maximization of submodular functions. A tutorial script illustrates the application of submodularity to machine learning and AI problems such as feature selection, clustering, inference and optimized information gathering.
Homepage: http://dl.acm.org/citation.cfm?id=1756044
Keywords: SFO toolbox; mathematical software; submodular function optimization; convex optimization
Related Software: GitHub; Matlab; SSD; Caffe; Jensen; OpenCV; Video2GIF; Vis-DSS; Python; apricot; Numba; CIFAR; Keras; Scikit; LLVM; SafeOpt; MouseTracker; Mousetrap; GPflow; TensorFlow
Referenced in: 8 Publications

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