Theorem 3.21 -- local optimality characterizes global optimality
ProvedDiscreteConvex.IntegralConvexity.integral_local_to_global_optimalitycombinatoricsconvex-optimizationdiscrete-convex-analysis
Theorem 3.21 (p.94). For an integrally convex function and ,
Global optimality is equivalent to a finite, uniformly bounded local check: only the (at most) neighbors reachable by adding on a subset of coordinates and on a disjoint subset need be checked, however large the effective domain. Restricting the right-hand side to a single fixed , or letting range over all of rather than just the unit sign pattern, would make the "if" direction trivial or the theorem false; the finite bound over disjoint is the entire content.
(Murota, Discrete Convex Analysis, SIAM 2003, DOI 10.1137/1.9780898718508, p.94, Theorem 3.21.)
Preamble
import Mathlib import Definitions.Def_DiscreteConvex_IntegralConvexity_IntegrallyConvex import Definitions.Def_DiscreteConvex_IntegralConvexity_DomZ import Definitions.Def_DiscreteConvex_IntegralConvexity_IndicatorVec
Formal statement
namespace DiscreteConvex.IntegralConvexity
/-- Theorem 3.21 (Murota, *Discrete Convex Analysis*, SIAM 2003, p.94). For an integrally
convex function `f : Zⁿ → R ∪ {+∞}` and `x ∈ dom_Z f`, global optimality of `x` is equivalent
to a finite local check over the (at most) `3ⁿ - 1` neighbors reachable by adding `+1` on a
subset `Y` of coordinates and `-1` on a disjoint subset `Z` of coordinates. -/
theorem integral_local_to_global_optimality {n : ℕ} (f : (Fin n → ℤ) → WithTop ℝ)
(hf : IntegrallyConvex f) (x : Fin n → ℤ) (hx : x ∈ DomZ f) :
(∀ y : Fin n → ℤ, f x ≤ f y) ↔
(∀ Y Z : Finset (Fin n),
f x ≤ f (fun i => x i + IndicatorVec Y i - IndicatorVec Z i)) := by sorry
end DiscreteConvex.IntegralConvexity
Source
Murota, Discrete Convex Analysis, SIAM 2003, DOI 10.1137/1.9780898718508, p.94, Theorem 3.21
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