Center for Automated Learning and Discovery
School of Computer Science, Carnegie Mellon University


A Note on the Budgeted Maximization
of Submodular Functions

Andreas Krause, Carlos Guestrin

March 2005


Keywords: Submodular functions, optimization, constraints, entropy maximization

Many set functions F in combinatorial optimization satisfy the diminishing returns property F(A ∪ X) − F(A) for ≥ F(A' ∪ X) − F(A') for A ⊂ A'. Such functions are called submodular. A result from Nemhauser states that the problem of selecting k-element subsets maximizing a nondecreasing submodular function can be approximated with a constant factor (1 − 1/e) performance guarantee. Khuller et. al. showed that for the special submodular function involved in the MAX-COVER problem, this approximation result generalizes to a budgeted setting under linear nonnegative cost-functions. In this note, we extend this result to general submodular functions. Motivated by the problem of maximizing entropy in discrete graphical models, where the submodular objective cannot be evaluated exactly, we generalize our result to account for absolute errors.

7 pages

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