The code on this webpage was developed by researchers of the SELECT Lab. Permission is granted to use this code for research purposes, although we request that you acknowledge these researchers in any published work, or equivalent distribution, using the bibtex entries provided. We make no warranties as to the quality or accuracy of any of the code linked from this page.
Submodular Function Optimization
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This MATLAB toolbox provides implementations of algorithms for maximizing and minimizing submodular set functions. It includes Queyranne's algorithm, Fujishige's minimum norm algorithm, Zhao et al's recursive splitting procedure, Nemhauser et al's greedy algorithm, Krause et al's Saturate algorithm, Goldengorin et al's Data Correcting algorithm, Narasimhan and Bilmes' submodular-supermodular procedure etc. It also provides a detailed tutorial script explaining the application of the toolbox to several machine learning problems like image denoising, clustering and experimental design. See also our tutorial materials.
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