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Proof of Theorem 4.1, p. 97 — the Lagrangian is minimized at φ_j = a_j + √(a_j/(y f_j))

Proved
KellyReversibility.Allocation.lagrangian_minimizer

by mikedeng1 · Oct 5, 2026 · Mathlib 0df444a (Lean v4.33.1)

capacity-allocationlagrange-multiplierp2o-batch-pfp2bp2o-gran-per-chapterp2o-plan-bookp2o-v1stochastic-networks

Let aj>0a_j > 0aj​>0 and fj>0f_j > 0fj​>0 for j=1,…,Jj = 1, \dots, Jj=1,…,J, let F∈RF \in \mathbb{R}F∈R and let y>0y > 0y>0 be a Lagrange multiplier. Over capacity vectors with ϕj>aj\phi_j > a_jϕj​>aj​ for all jjj, the Lagrangian

L(ϕ)=∑jajϕj−aj+y(∑jfjϕj−F)L(\phi) = \sum_j \frac{a_j}{\phi_j - a_j} + y\Big(\sum_j f_j\phi_j - F\Big)L(ϕ)=j∑​ϕj​−aj​aj​​+y(j∑​fj​ϕj​−F)

is minimized by the choice

ϕjy=aj+ajyfj,\phi^y_j = a_j + \sqrt{\frac{a_j}{y f_j}},ϕjy​=aj​+yfj​aj​​​,

which satisfies ϕjy>aj\phi^y_j > a_jϕjy​>aj​, and every other vector ϕ\phiϕ with ϕj>aj\phi_j > a_jϕj​>aj​ for all jjj has L(ϕ)>L(ϕy)L(\phi) > L(\phi^y)L(ϕ)>L(ϕy).

This is the unconstrained step of the Lagrangian method used to prove Theorem 4.1.

Formalization Note The book says "LLL is minimized by the choice"; the strict inequality for every other point (uniqueness of the minimizer) is a slight strengthening, true because LLL is strictly convex on the region ϕj>aj\phi_j > a_jϕj​>aj​. The minimization is over the open region ϕj>aj\phi_j > a_jϕj​>aj​, where every term of LLL is finite.

Preamble
import Mathlib
import Definitions.Def_KellyReversibility_Allocation_CapacityAllocation
Formal statement
namespace KellyReversibility.Allocation

theorem lagrangian_minimizer {J : ℕ} (a f : Fin J → ℝ) (F y : ℝ)
    (ha : ∀ j, 0 < a j) (hf : ∀ j, 0 < f j) (hy : 0 < y) :
    let φstar : Fin J → ℝ := fun j => a j + Real.sqrt (a j / (y * f j))
    (∀ j, a j < φstar j) ∧
      ∀ φ : Fin J → ℝ, (∀ j, a j < φ j) → φ ≠ φstar →
        lagrangian a f F y φstar < lagrangian a f F y φ := by sorry

end KellyReversibility.Allocation
Source
Kelly, Reversibility and Stochastic Networks, Wiley 1979, p. 97, proof of Theorem 4.1 (the Lagrangian L and the choice φ_j = a_j + √(a_j/(y f_j)))
Human review
  • Endorsed by Shuze Chen · Oct 5, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Oct 5, 2026

    Confirmed by the mission captain (proposal self-audit).

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