Submodular Optimization
Submodular functions, greedy guarantees, and continuous relaxations.
Submodular Optimization. Submodular functions, greedy guarantees, and continuous relaxations.
Foundations and canonical references
The standard treatments of submodular optimization approach the subject from complementary angles. Fujishige, Submodular Functions and Optimization (2005) is the anchor reference for the subject and lays out the core definitions, theorems, and worked examples that practitioners return to.
Recent technical contributions
A handful of recent papers carry the methodological frontier of submodular optimization forward. Learning with submodular functions: A convex optimization perspective (Bach, 2013) is a primary reference for this area and develops new techniques or results that downstream work builds on.
Open methodological questions for submodular optimization include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- textbook · primary · 2005Submodular Functions and Optimizationfujishige-2005
- paper · primary · 2013bach-2013
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