Abstract
We devise a method for proving inequalities on submodular functions, with a term rewriting flavor. Our method comprises of the following steps: (1) start with a linear combination X of the values of the function; (2) define a set of simplification rules; (3) conclude that X≥Y, where Y is a linear combination of a small number of terms which cannot be simplified further; (4) calculate the coefficients of Y by evaluating X and Y on functions on which the inequality is tight. The crucial third step is non-constructive, since it uses compactness of the dual cone of submodular functions. Its proof uses the classical uncrossing technique with a quadratic potential function. We prove several inequalities using our method, and use them to tightly analyze the performance of two natural (but non-optimal) algorithms for submodular maximization, the random set algorithm and local search.
Original language | English |
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Pages (from-to) | 457-464 |
Number of pages | 8 |
Journal | Information Processing Letters |
Volume | 113 |
Issue number | 13 |
DOIs | |
State | Published - 2013 |
Externally published | Yes |
Keywords
- Analysis of algorithms
- Inequalities
- Local search
- Submodularity
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Signal Processing
- Information Systems
- Computer Science Applications