Let be a normalised set function that is a submodular set function, and order a finite subset . Set and . Then the displayed inequality holds for every , with equality for . The right side is a modular set function lying below on the power set of and touching it at the top.
Proof. Reveal the elements of in the chosen order. Immediately before adding , the conditioning set is . By diminishing returns, its marginal increment is at least . Sum and use . The same telescoping sum gives equality at . The individual increments need not be nonnegative.
Suppose is a normalised set function and a submodular set function, and cover every element of exactly times, counting repetitions in the indexed collection. Then the displayed inequality holds. Neither the monotone set function property nor the nonnegative function property is required.
Proof. Fix an ordering of and write for its successive marginal increments. The chain lower bound for a submodular set function gives . Summing counts each exactly times, and . Exact coverage matters because the may be negative. For a monotone set function, the same argument also allows each element to occur at least times.
Hypergraph cut function 2026-10-25
For a weighted hypergraph, the hypergraph cut function totals the weights of hyperedges meeting both a subset and its complement. It is a normalised set function and a symmetric set function. With nonnegative weights it is a nonnegative function and a submodular set function. It is generally not a monotone set function: a single two-element hyperedge of weight one has cut value one on either singleton, but zero on the whole ground set.
Proof of submodularity for nonnegative weights. For one hyperedge of size at least two, let indicate whether it is split. Adding has zero marginal increment. For , with , the marginal increment is if is empty, if it equals , and zero otherwise. These values can only decrease under inclusion of . Thus diminishing returns holds for , and taking a nonnegative weighted sum preserves it. For a singleton or empty hyperedge, is identically zero. A negative-weight two-element hyperedge gives a counterexample to submodularity: its two singleton values sum to a negative number, while the values at their union and intersection are zero.
Modular set function 2026-10-25
On a finite ground set, a modular set function is a set function satisfying the displayed equality for every pair of subsets. Equivalently,To prove this representation, apply the equality to disjoint and : . Add these fixed marginal increments along any ordering of . Conversely, the contributions of each element and of the constant cancel in the displayed identity. Thus a modular set function need not be a normalised set function; its normalised instances are precisely the real-valued set functions with finite additivity on a finite power set. Adding or subtracting a modular set function preserves submodularity.
Every real-valued submodular set function on a finite ground set is the sum of a normalised set function that is nonnegative, monotone and submodular, and a modular set function . A useful explicit choice isProof. The formula makes a modular set function, so is a submodular set function. It is a normalised set function because . For , diminishing returns gives , hence . Thus is a monotone set function, and starting at zero makes it a nonnegative function. The decomposition separates all potentially negative terminal marginal increments into the simple additive term. A constant term in is necessary unless is already a normalised set function.
Monotone set function 2026-10-25
A real-valued set function is monotone, in the nondecreasing convention, when enlarging its input subset cannot decrease its value. Equivalently, each marginal increment is nonnegative. This follows by adding elements individually; the converse follows by considering . A normalised set function with this property is a nonnegative function.
- A normalised set function satisfies .
- A nonnegative function satisfies for every .
- A monotone set function satisfies whenever .
- A symmetric set function satisfies for every .
Here monotone set function means nondecreasing under inclusion; it does not require strict increase.
Use the chain lower bound for a submodular set function. Order , set with , and put . For each , reveal its elements in this same order. Since , diminishing returns and the normalised set function property giveApply this to each and interchange the finite sums. Every element occurs exactly times, soThis is the exact-cover inequality for submodular set functions. Some can be negative: exact multiplicity, rather than merely at least occurrences, is what makes the argument valid without monotonicity. In fact the nonnegative function hypothesis is unnecessary here. If , every is empty and both sides vanish.
The monotone decomposition of a submodular set function subtracts each element's smallest possible marginal increment. For , defineThe set function is a modular set function: the contributions of each element, and the constant , cancel inSubtracting this equality from the submodularity inequality for proves that is a submodular set function. Also , so it is a normalised set function.
For , the inclusion and diminishing returns implyAdding elements one at a time now proves that is a monotone set function. Since it starts at zero, it is also a nonnegative function. Thus has all the required properties.
Here modular set function means equality in the intersection–union identity; a constant term is allowed. If one imposed the additional convention , the claimed decomposition of an arbitrary would require . The displayed constant is essential for the problem as stated. For , take and .
Submodularity of matroid rank 2026-10-25
The rank of a matroid is a normalised set function, a monotone set function, and a submodular set function. Adding one element increases it by at most one.
Proof of submodularity. Choose a maximal matroid independent set in , extend it to a maximal matroid independent set in , then extend it to one in . The matroid independence axioms make their sizes , and . Every element of lies outside , since otherwise it could be added to inside . Hence is an independent subset of , so its size is at most .
The remaining assertions follow from the definition of the rank of a matroid. The empty set has maximum independent size zero, increasing the available subset cannot reduce that maximum, and deleting a newly added element from an independent subset decreases its size by at most one.