Rewarding Maps: On Greedy Optimization of Set Functions

Given a set function, that is, a map ƒ: (E) → {−∞} from the set (E) of subsets of a finite set E into the reals including −∞, the standard greedy algorithm (GA) for optimizing ƒ starts with the empty set and then proceeds by enlarging this set greedily, element by element. A set function ƒ is said to be tractable if in this way a sequence x₀ , x₁, . . ., x_N E (N #E) of subsets with max(ƒ) {ƒ(x₀), ƒ(x₁), . . ., ƒ(x_N)} will always be found. In this note, we will reinterpret and transcend the traditions of classical GA-theory (cf., e.g., KLS]) by establishing necessary and sufficient conditions for a set function ƒ not just to be tractable as it stands, but to give rise to a whole family of tractable set functions ƒ(η) : (E) → : x ƒ(x) + Σ_{e x}η(e), where η runs through all real valued weighting schemes η : E → , in which case ƒ will be called rewarding. In addition, we will characterize two important subclasses of rewarding maps, viz. truncatably rewarding (or well-layered) maps, that is, set functions ƒ such that formula] is rewarding for every i = 1, . . ., N, and matroidal maps, that is, set functions ƒ such that for every η : E → and every ƒ_eta-greedy sequence x₀, x₁, . . ., x_N as above, one has max(ƒ_η) = ƒ_η(x_i) for the unique i {0, . . ., N} with ƒ_η(x₀) < ƒ_η(x₁) < ··· < ƒ_η(x_i) ≥ ƒ_η(x_{i + 1}).