Operations Research

Motivation

The traveling-salesman problem (TSP) asks for a cheapest cycle through all vertices of a weighted complete graph. Exact algorithms for it are branch-and-bound searches, and their size depends almost entirely on the quality of the lower bounds used to prune the search. In 1970 Held and Karp introduced the 1-tree bound: a Lagrangian relaxation of the degree-2 constraints of a tour, whose value can be evaluated by one minimum-spanning-tree computation (Held & Karp, Part I, 1970). Part II (Held & Karp, 1971) replaces the ascent procedure of Part I by an iterative method related to the relaxation method for linear inequalities of Agmon and of Motzkin and Schoenberg (1954), and with it solved to proven optimality every instance presented to it, up to 64 cities. The iteration is the prototype of what is now called the subgradient method with Polyak-type step sizes, and the 1-tree bound remains a standard lower bound in exact TSP codes.

Timeline:

• 1954 — Agmon; Motzkin and Schoenberg: the relaxation method for systems of linear inequalities, and convergence of Féjer-monotone sequences relative to full-dimensional sets.
• 1970 — Held and Karp (Part I): the 1-tree bound $\max_\pi w(\pi)$ and a column-generation / ascent method for it.
• 1971 — Held and Karp (Part II): the iteration $\pi^{m+1} = \pi^m + t_m v_{k(\pi^m)}$, its relaxation-method analysis (Lemmas 1–3) and the constant-step guarantee (Theorem 1).
• 1974 — Held, Wolfe and Crowder validate the method as general subgradient optimization.

Setting

Let $n \ge 3$ and let $(c_{ij})$ be a symmetric real $n\times n$ matrix of weights on the edges of the complete graph $K_n$ with vertex set $\{1,\dots,n\}$; weights may be negative and need not satisfy the triangle inequality. A subgraph has weight equal to the sum of its edge weights. A tour is a cycle through every vertex exactly once; $C^*$ is the weight of a minimum tour.

A 1-tree is a tree on the vertex set $\{2,\dots,n\}$ together with two distinct edges at vertex $1$. Index the 1-trees by $k$; let $c_k$ be the weight of the $k$-th 1-tree, $d_{ik}$ the degree of vertex $i$ in it, and $v_k \in \mathbb R^n$ the degree-excess vector with components $d_{ik}-2$. For $\pi \in \mathbb R^n$ define

$$w(\pi) = \min_k\,[c_k + \pi\cdot v_k].$$

A tour is a 1-tree with $v_k = 0$, so $C^* \ge w(\pi)$ for every $\pi$ (Eq. (2)); the best bound is $\max_\pi w(\pi)$. For a point $\pi$, $k(\pi)$ denotes a minimum-weight 1-tree at $\pi$, a 1-tree attaining the minimum defining $w(\pi)$. The ascent iteration (3) is

$$\pi^{m+1} = \pi^m + t_m\,v_{k(\pi^m)}.$$

For a target value $\bar w$, $P_{\bar w}$ is the polyhedron of solutions of $\bar w \le c_k + \pi\cdot v_k$ for all $k$ (system (5)). All norms $\|\cdot\|$ are Euclidean.

Formalization targets

Goal: Theorem 1

With constant step $t_m = \bar t > 0$, any starting point and any choice of minimum-weight 1-trees,

$$\sup_m w(\pi^m) \;\ge\; \max_\pi w(\pi) - \tfrac12\,\bar t\,\limsup_{m\to\infty}\|v_{k(\pi^m)}\|^2 .$$

Milestones

1. Eq. (2): $C^* \ge w(\pi)$ for every $\pi$.
2. Lemma 1: if $w(\bar\pi) \ge w(\pi)$ then $(\bar\pi-\pi)\cdot v_{k(\pi)} \ge w(\bar\pi) - w(\pi) \ge 0$.
3. Lemma 2: if $0 < t < 2(w(\bar\pi)-w(\pi))/\|v_{k(\pi)}\|^2$ then $\|\bar\pi - (\pi + t v_{k(\pi)})\| < \|\bar\pi-\pi\|$.
4. Féjer-monotone convergence (Motzkin–Schoenberg, quoted in the proof of Lemma 3): a sequence whose distance to every point of a set with nonempty interior is nonincreasing converges.
5. Lemma 3, Case 1: for $\bar w < \max_\pi w$ and the relaxation iteration

$$\pi^{m+1} = \pi^m + \lambda_m\,\frac{\bar w - w(\pi^m)}{\|v_{k(\pi^m)}\|^2}\,v_{k(\pi^m)} \qquad (6)$$

with $0<\varepsilon<\lambda_m\le 2$, the iterates enter $P_{\bar w}$ or converge to a boundary point of $P_{\bar w}$. 6. Lemma 3, Case 2: with $\lambda_m = 2$ the iterates enter $P_{\bar w}$. 7. §3 bound: the restricted minimum $w_{X,Y}(\pi)$ over 1-trees containing the edges $X$ and avoiding the edges $Y$ is a lower bound on every tour of the derived problem.

Milestones 1–5 are the steps of the paper's proof of Theorem 1; 6 and 7 are further results of the paper on the same objects.

Significance

Theorem 1 is the paper's justification of the step rule actually used in its computations: a fixed step $\bar t$ loses at most $\tfrac12\bar t$ times the asymptotic squared deviation of the generated 1-trees from being tours. Since $\|v_k\|^2$ is an even integer that vanishes exactly on tours, and the paper observes it is typically small in practice, the bound explains why the constant-step ascent reaches bounds sharp enough for branch-and-bound. Lemmas 1–3 are the first analysis of a subgradient-type method for a nonsmooth concave function, cast as the relaxation method for the (exponentially large) system (5).

All results are proved in the paper (except the Féjer-monotone convergence and Case 2 of Lemma 3, which it cites from Motzkin and Schoenberg). None is formalized: the platform has the 1-tree lower bound only under a metric assumption on the weights (SupplyChainTheory.held_karp_bound, SupplyChainTheory.one_tree_lower_bound), and the subtour-LP bound MetricTSP.held_karp_le_opt, a different object. This mission produces the bound for arbitrary real weights, the supergradient property of $v_{k(\pi)}$, and a machine-checked convergence analysis of the relaxation iteration.

Difficulty

Lemmas 1 and 2 and Eq. (2) are short once the finite minimum defining $w$ is handled. The difficulty is in Lemma 3 and Theorem 1. The iteration is not monotone in $w$, so no descent argument applies; progress is measured by the Euclidean distance to the target polyhedron $P_{\bar w}$, and turning distance decrease into convergence requires the Féjer-monotonicity theorem, which in turn needs $P_{\bar w}$ to have nonempty interior (from $\bar w < \max_\pi w$). In Theorem 1 the step is constant rather than of the relaxation form (6), and the target polyhedron is not given in advance: the relevant relaxation parameters are admissible only eventually and must be kept away from zero, which requires controlling degenerate directions $v_{k(\pi^m)} = 0$ and the behaviour of $w$ along an iteration that is not known a priori to stay bounded. A direct argument that $w(\pi^m)$ increases fails, since single steps can decrease $w$.

Formalization scope

Vertices are Fin n, the paper's vertex 1 is 0 : Fin n, and graphs are SimpleGraph (Fin n). Weights are c : Sym2 (Fin n) → ℝ, arbitrary reals. A 1-tree is a graph whose restriction to the vertices other than 0 is a tree and in which 0 has degree 2; a tour is a connected graph with all degrees 2. w(π) is the minimum of weight c G + ∑ i, π i * (deg G i − 2) over 1-trees, written as an sInf over a finite set that is nonempty for n ≥ 3; every theorem assumes 3 ≤ n. Euclidean norms and inner products are written as coordinate sums of squares and products, never Mathlib's sup norm on Fin n → ℝ. The minimum-weight 1-tree $k(\pi^m)$ is a hypothesis at every step, and ties may be broken arbitrarily. The goal is stated as "for every $\pi^*$ and $\delta>0$ some iterate has $w(\pi^m) > w(\pi^*) - \tfrac12\bar t L - \delta$", which is equivalent to the printed inequality; $L$ is the limsup of a sequence with finitely many values, a genuine real.

The statements exclude trivializing readings: the 1-tree predicate rejects graphs without exactly two edges at vertex 1; w is never a minimum over an empty set under the standing hypothesis 3 ≤ n; no ⨆ of a possibly unbounded family is used; the step size $\bar t$ and the parameter $\varepsilon$ are strictly positive.

A complete development needs finite minima of affine functions (concavity, attainment), 1-tree and tour combinatorics on simple graphs, and Féjer-monotone sequences in $\mathbb R^n$; the last two are reusable beyond this mission. Proofs of any milestone, and alternative arguments for Lemma 3, are welcome.

Selected references

• M. Held and R. M. Karp, The traveling-salesman problem and minimum spanning trees: Part II, Mathematical Programming 1 (1971) 6–25. https://doi.org/10.1007/BF01584070
• M. Held and R. M. Karp, The traveling-salesman problem and minimum spanning trees, Operations Research 18 (1970) 1138–1162. https://doi.org/10.1287/opre.18.6.1138
• T. S. Motzkin and I. J. Schoenberg, The relaxation method for linear inequalities, Canadian Journal of Mathematics 6 (1954) 393–404. https://doi.org/10.4153/CJM-1954-038-x
• S. Agmon, The relaxation method for linear inequalities, Canadian Journal of Mathematics 6 (1954) 382–392. https://doi.org/10.4153/CJM-1954-037-2
• M. Held, P. Wolfe and H. P. Crowder, Validation of subgradient optimization, Mathematical Programming 6 (1974) 62–88. https://doi.org/10.1007/BF01580223

Motivation

This mission continues chapter 11's account of the M♮-concave/M♮-convex exchange-economy model begun in mission 14-economic-equilibrium, placing seven of that chunk's own results that were previously left out-of-cone: the two gross-substitutes-style characterizations of M♮-concavity (§11.3), the transfer theorem that lifts an equilibrium of the continuous relaxation to one for indivisible commodities (§11.4), and the explicit polyhedral description of the equilibrium price set together with its feasibility criterion (§11.5).

Setting

Mission 14-economic-equilibrium built the exchange-economy vocabulary this mission redeclares in full (UDom, ArgMaxBot/ArgMinTop, PriceShift/PriceShiftConvex, DemandSet/SupplySet, IsEquilibrium, MNaturalConcave, IsMNaturalConvexSet, the concave/convex closures ConcaveClosureR/ConvexClosureR and their continuous analogues ContDemandSet/ContSupplySet/ IsContEquilibrium) and placed the qualitative structural theorems (Theorems 11.1-11.3, 11.4, 11.16-11.18, 11.23-11.24). This mission adds the gross-substitutes axioms (−M♮-GS[Z], the price-monotonicity property NegGS, and −M♮-SWGS[Z], its one-price-at-a-time refinement NegSWGS), the M♮-convex-set transfer machinery connecting a continuous equilibrium to a discrete one, and the equilibrium price polyhedron built from the three bound families ℓ(j), u(j), u(i,j) (Eqs. (11.40)-(11.42)) that make Theorem 11.16's qualitative L♮-convex-polyhedron fact concrete and linear-programming-checkable.

Formalization targets

Goal: The equilibrium price set is the explicit L♮-convex polyhedron (11.43) (Theorem 11.21)

For a fixed allocation (x,y), the set P* of all equilibrium price vectors is an L♮-convex polyhedron and equals the polyhedron cut out by max{0,ℓ(j)} ≤ p(j) ≤ u(j) and p(j)-p(i) ≤ u(i,j). Chosen as goal: it is the sharpest structural result of chapter 11's computation section, upgrading Theorem 11.16's qualitative fact to a concrete description, and is what Theorem 11.22 (also placed) builds on directly.

Supporting structural targets

Theorem 11.5 and Theorem 11.6 characterize M♮-concavity via the gross-substitutes and stepwise gross-substitutes properties, completing chapter 11's suite of M♮-concavity characterizations begun with Theorem 11.4 (mission 14). Theorem 11.15 is the general transfer theorem (continuous equilibrium ⟹ discrete equilibrium) that mission 14's own Theorem 11.14 invokes as a special case. Theorem 11.22 gives the feasibility criterion for the existence of an equilibrium price vector, the mission's second theorem built on the equilibrium price polyhedron.

Significance

Together with mission 14-economic-equilibrium, this mission completes the book's account of how M♮-concavity/convexity — a purely combinatorial exchange condition — reproduces, and sharpens, the classical gross-substitutes theory of competitive equilibrium for economies with indivisible goods: existence transfers from the continuous relaxation, and the equilibrium price set itself has a description exact enough to reduce to a linear feasibility question. None of these results are open — they are Murota's own account (attributed in the book's own notes to Danilov-Koshevoy- Lang and Murota-Tamura for the gross-substitutes theorems, and to Murota-Tamura for the equilibrium price polyhedron); this mission contributes a faithful, machine-checked formal statement of each (see Formalization scope).

Difficulty

Two of this chunk's seven BRIEF.md results are not drafted this pass, for a disclosed time- budget reason rather than any faithfulness failure: Proposition 11.19 and Theorem 11.20 require the H,L-indexed bipartite MSFP2 flow-network vocabulary (separate vertex sets V+_e, V+_l, V-_h, an M-convex/M-concave-combining flow objective) that neither this mission nor mission 14 builds, and building it in proportion to placing exactly these two results was judged disproportionate to the remaining time in this pass; see HARD.md and STATUS.md. This is explicitly not a hard exclusion — both results are well-posed and provable from the book's own complete proofs — and is recorded as an honest scope limitation for a future pass. Theorem 11.22's own trailing algorithmic remark (that equilibrium prices can be found via a shortest-path computation, yielding a polynomial-time equilibrium-checking algorithm) is a computational/ complexity claim outside this series' propositional-formalization methodology and is omitted; the mathematical "iff feasibility" content is placed in full. See HARD.md.

Formalization scope

Ground set K is a Fintype with DecidableEq; consumer/producer index sets H, L are Fintypes (Nonempty where the price-bound formulas (11.40)-(11.42) need a nonempty sup'/inf' range). All base vocabulary is redeclared fresh from mission 14-economic-equilibrium's own definitions, since this draft cannot import that sibling mission. The gross-substitutes axioms are formalized directly from their defining inequalities (Eqs. preceding (11.19) and following, and p.331); the equilibrium price polyhedron's bound families ℓ(j)/u(j)/u(i,j) are formalized literally from Eqs. (11.40)-(11.42), extracting each WithBot ℝ/WithTop ℝ operand to ℝ before subtracting (since WithBot ℝ carries no subtraction instance). Two results (Proposition 11.19, Theorem 11.20) are not drafted this pass for the disclosed time-budget reason above; one result (Theorem 11.22's trailing algorithmic remark) is scoped out as computational content. Contributions completing any of the five sorrys, or building the MSFP2 vocabulary to place Proposition 11.19/Theorem 11.20 in a follow-up mission, are welcome.

Selected references

• K. Murota, Discrete Convex Analysis, SIAM, 2003. DOI: 10.1137/1.9780898718508.
• V. Danilov, G. Koshevoy, K. Murota, "Discrete convexity and equilibria in economies with indivisible goods and money," Mathematical Social Sciences, 41 (2001), pp. 251-273 [33] (origin of the gross-substitutes characterization, Theorem 11.6).
• K. Murota, A. Tamura, "Application of M-convex submodular flow problem to mathematical economics," Japan Journal of Industrial and Applied Mathematics, 20 (2003), pp. 257-277 [160] (origin of the equilibrium price polyhedron, Theorems 11.20-11.22).

Motivation

Two strategic levers shape the balance of power between a manufacturer and the retailer that resells its product. The first is strategic inventory: a buyer that orders more than it sells today, and carries the surplus into the next period, weakens the supplier's leverage over tomorrow's wholesale price. Anand, Anupindi and Bassok (Management Science 2008) showed that in a two-period channel the buyer withholds inventory exactly when its unit holding cost is below $\alpha/4$. The second is supplier encroachment: a supplier that can sell directly to consumers competes with its own buyer. Arya, Mittendorf and Sappington (Marketing Science 2007) showed that the threat of encroachment can lower wholesale prices and benefit both parties.

Guan, Gurnani, Geng and Luo, Strategic Inventory and Supplier Encroachment (MSOM 2019), combine the two levers in one game. Their headline qualitative finding is Proposition 4.2. When the supplier's direct channel is costly, but not quite too costly to use, the buyer keeps withholding inventory at every finite holding cost. This contrasts with the $\alpha/4$ cutoff of Anand et al. This mission formalizes that proposition, together with the equilibrium characterizations of Appendix A on which it rests.

Setting

There is one supplier and one buyer, two periods, deterministic demand and complete information. In each period the market price is $p = \alpha - q$, where $q$ is the total quantity sold in that period and $\alpha > 0$ is the demand intercept. The buyer pays a per-unit holding cost $h \ge 0$ on inventory carried into period 2. The supplier pays a per-unit direct selling cost $s \ge 0$. All other costs and the salvage value are zero. The moves are:

1. The supplier quotes a wholesale price $w_1 \ge 0$.
2. The buyer orders $Q_1$ and sells $q_1$, with $0 \le q_1 \le Q_1$. It carries the inventory $I = Q_1 - q_1$ into period 2.
3. The supplier quotes $w_2 \ge 0$.
4. The buyer orders $Q_2 \ge 0$ and sells $q_2$, with $0 \le q_2 \le I + Q_2$.
5. Having observed everything, the supplier sells $q_s \ge 0$ directly. The period-2 price is $\alpha - q_2 - q_s$.

The total profits are

$$\Pi_b = (\alpha - q_1)q_1 - w_1 Q_1 - hI + (\alpha - q_2 - q_s)q_2 - w_2 Q_2, \qquad \Pi_s = w_1 Q_1 + w_2 Q_2 + (\alpha - q_2 - q_s - s)q_s .$$

A strategy profile assigns an action to every history at which a player moves. It is a subgame perfect equilibrium (SPE) if, at every feasible history, the mover's prescribed action is feasible and no feasible alternative, followed by the profile afterwards, raises the mover's total profit. The equilibrium path is the outcome the profile generates; the equilibrium inventory is $I$ on that path. Following the paper (§4), the goal theorem sets $\alpha = 1$.

Formalization targets

Goal: Proposition 4.2

With $\alpha = 1$,

$$\forall h > 0\ \ \exists \epsilon > 0\ \ \forall s \in \big(\tfrac56 - \epsilon, \tfrac56\big):\quad \text{an SPE exists, and every SPE has } I > 0 \text{ and } hI < \tfrac{11}{72}.$$

Here $\epsilon$ may depend on $h$, and the bound $11/72$ applies at the same $(h, s)$.

Milestones, in attack order

1. Stage-3 best response (§3.1.1): in every SPE, the supplier sells $q_s = (\alpha - q_2 - s)^+/2$ at every history.
2. Eq. (1) (§3.1.1): with no inventory, the buyer's period-2 quantity is the four-branch function $q_b(w)$ of the period-2 wholesale price $w$.
3. Proposition 4.1, existence half: an SPE exists for every $h \ge 0$ and $s \ge 0$.
4. Region 8 (Tables A.1 and A.4): for $h < h_{11}$ with $s_3 \le s < 5\alpha/6$, or for $h < \alpha/4$ with $5\alpha/6 \le s < \alpha$, every SPE has $I = 5(\alpha - 4h)/34$ and the path of Table A.4.
5. Region 7, second part (Tables A.1 and A.3): for $h_{11} < h < h_{10}$ and $s_3 \le s < 5\alpha/6$, every SPE has $I = I^* = (2\alpha - 3s + x)/2$ and the path of Table A.3.
6. Region 10, second part (Tables A.1 and A.4): for $h > h_7$ and $2\alpha/3 < s < 5\alpha/6$, every SPE has $I = 0$.

The thresholds $x$, $s_3$, $h_7$, $h_{10}$, $h_{11}$ are explicit algebraic functions of $s$, given in Appendix A.

Significance

Proposition 4.2 separates the combined model from the two models it merges. Without a direct channel, inventory disappears once $h \ge \alpha/4$. With a direct channel, a threat of encroachment that is only barely credible keeps inventory alive at every holding cost. The total holding cost nevertheless stays below a constant, because the inventory shrinks as $h$ grows. Milestone 6 shows the flip side: for a fixed $s < 5\alpha/6$, a large enough $h$ removes the inventory. This is why the window $\epsilon$ must depend on $h$.

The paper's proofs are in an online appendix that is not reproduced in the article. The article itself gives the equilibrium only as tables. A formal development would supply a checked backward-induction proof of those tables in the regions near $s = 5\alpha/6$. It would also give a machine-checked SPE framework for multi-stage pricing-and-quantity games in supply chains, which none of the following exists for: Stackelberg pricing, sequential quantity competition, or dual-channel encroachment. No part of this paper has been formalized before.

Difficulty

The equilibrium is found by backward induction through five stages. Each stage's value function is only piecewise smooth, because the supplier's direct-channel response $(\cdot)^+$ switches on and off. As a result the buyer's period-2 profit has kinks, the supplier's period-2 profit as a function of $I$ has several local maxima, and the period-1 problems must compare branches whose boundaries are the irrational thresholds of Appendix A. The obvious approach, solving the first-order conditions stage by stage, fails at the kinks. At some region boundaries it also misses that the supplier is indifferent between two first-period prices, which makes the equilibrium path change discontinuously. Proposition 4.2 then needs uniform control of $h_7$, $h_{10}$ and $h_{11}$ as $s \uparrow 5/6$, where the denominator $3s - 2 - x$ of $h_7$ tends to $0$.

Formalization scope

• Representation. Strategies are functions of the full history, not Markov rules in $I$. IsSPE imposes optimality at every feasible history, including off-path ones. All actions are real numbers; wholesale prices and quantities are nonnegative, with $q_1 \le Q_1$ and $q_2 \le I + Q_2$.
• Normalization. The goal instantiates $\alpha = 1$ as the paper does from §4 on. The milestones keep a general $\alpha > 0$.
• Uniqueness is not stated. Proposition 4.1's uniqueness claim is false for strategy profiles: after the off-path price $w_2 = 0$, every order $Q_2 \ge q_2 - I$ is optimal. The goal and the region milestones therefore quantify over every SPE and carry existence as a separate conjunct.
• Corrected hypotheses.
  • The printed $s_3 = (\sqrt{(37 - 3\sqrt{65})/34} + 4/6)\alpha \approx 1.28\alpha$ is replaced by $(\sqrt{37 - 3\sqrt{65}}/34 + 4/6)\alpha \approx 0.772\alpha$, which matches the paper's "≈0.77".
  • Eq. (1) excludes the corner $w = 0$, $s < \alpha/3$, where its second branch is wrong.
  • Region 8 drops its boundary $h = h_{11}$ and Region 7 drops $h = h_{10}$, because the equilibrium path switches there and need not be unique.
• Ruled-out trivialization. The inventory in the goal is the inventory on the path of an SPE of the game above. It is not the closed form $5(1 - 4h)/34$ or $I^*$ from the tables, and the regional characterization is not a hypothesis. Otherwise the goal would reduce to algebra about the thresholds.
• Infrastructure and contributions. The definitions Game, IsSPE and Thresholds are shared by every statement. Welcome contributions include:
  • lemmas on maximizing concave piecewise-quadratic functions on half-lines;
  • a reusable backward-induction lemma for finite-stage games with real action sets;
  • interval-arithmetic facts about $x$, $h_7$, $h_{10}$ and $h_{11}$ near $s = 5/6$;
  • proofs of the paper's other regions.

Selected references

• T. Guan, H. Gurnani, X. Geng, Y. Luo, Strategic Inventory and Supplier Encroachment, Manufacturing & Service Operations Management 21(3):536–555, 2019. https://doi.org/10.1287/msom.2018.0705
• K. Anand, R. Anupindi, Y. Bassok, Strategic Inventories in Vertical Contracts, Management Science 54(10):1792–1804, 2008. https://doi.org/10.1287/mnsc.1080.0894
• A. Arya, B. Mittendorf, D. Sappington, The Bright Side of Supplier Encroachment, Marketing Science 26(5):651–659, 2007. https://doi.org/10.1287/mksc.1070.0280

Motivation

Competitive-equilibrium theory for economies of divisible commodities — where consumption and production are real vectors — has rested on a rigorous mathematical foundation since around 1960, built from convexity, compactness, and fixed-point theorems (Debreu 1959; Arrow–Hahn 1971; McKenzie 2002). A large share of real markets, however, trade goods that cannot be split: houses, cars, aircraft, job assignments, radio spectrum licenses. For such economies, no comparably general existence theory existed before the framework this mission formalizes. Kelso and Crawford (1982) and Gul and Stacchetti (1999) had identified the gross substitutes property as the right condition on preferences for equilibrium to exist in labor-market and assignment models; Danilov, Koshevoy, and Murota (1998, 2001) showed that gross substitutes, and several other conditions proposed independently in the economics literature, all coincide with a single combinatorial notion from discrete convex analysis: M-natural-concavity. This mission formalizes the resulting existence theorem for economies with indivisible goods, together with the definitional results that pin down exactly what M-natural-concavity of a utility function means and how it connects to the classical demand-set language of general equilibrium theory.

Setting

Fix a finite set $K$ of indivisible commodity types and a finite set $H$ of consumers ("she"). A consumption bundle is an integer vector $x \in \mathbb Z^K$, one coordinate per commodity. Consumer $h$'s preferences over bundles, net of a perfectly divisible numeraire ("money"), are summarized by a utility function $U_h : \mathbb Z^K \to \mathbb R \cup \{-\infty\}$, where $-\infty$ marks bundles outside her feasible range (her effective domain, $\operatorname{dom} U_h = \{x : U_h(x) \ne -\infty\}$). Given a price vector $p \in \mathbb R^K$ (one real price per commodity), consumer $h$ chooses a bundle from her demand set

$$D_h(p) = \arg\max_{x \in \mathbb Z^K} \big(U_h(x) - \langle p, x\rangle\big),$$

the bundles that maximize utility net of expenditure; the shorthand $U_h[-p](x) := U_h(x) - \langle p,x\rangle$ is used throughout. For the notion this mission is built around, write $\chi_i \in \mathbb Z^K$ for the $i$-th unit vector, and for $x, y \in \mathbb Z^K$ let $\operatorname{supp}^+(x-y) = \{k : x(k) > y(k)\}$ and $\operatorname{supp}^-(x-y) = \{k : x(k) < y(k)\}$. A function $U$ with nonempty effective domain is M-natural-concave if it satisfies the exchange axiom: for $x, y \in \operatorname{dom} U$ and $i \in \operatorname{supp}^+(x-y)$,

$$U(x) + U(y) \le \max\Big(U(x-\chi_i)+U(y+\chi_i),\ \max_{j \in \operatorname{supp}^-(x-y)} \big[U(x-\chi_i+\chi_j) + U(y+\chi_i-\chi_j)\big]\Big),$$

with the convention that a maximum over the empty set is $-\infty$. A producer $l$ from a finite set $L$ is symmetric, described by a cost function $C_l : \mathbb Z^K \to \mathbb R \cup \{+\infty\}$ that is M-natural-convex — the mirror-image exchange axiom with $\min$ in place of $\max$ and the inequality reversed — and a supply set $S_l(p) = \arg\max_y(\langle p,y \rangle - C_l(y))$. An equilibrium for a total initial endowment $x^\circ \in \mathbb Z^K$ is a tuple $((x_h \mid h \in H), (y_l \mid l \in L), p)$ with $x_h \in D_h(p)$, $y_l \in S_l(p)$, market clearing $\sum_h x_h = x^\circ + \sum_l y_l$, and $p \ge 0$.

Formalization targets

$$\textbf{Theorem 11.13 (goal).}\quad \text{If every } U_h \text{ is nondecreasing and M-natural-concave with bounded domain, an equilibrium exists for every } x^\circ \in \bigcap_h \operatorname{dom} U_h \text{ in the exchange economy } (L = \emptyset).$$

This is the weakest form of the existence claim the mission proves in full — no producers, so no interaction between two different M-natural-convexity classes is needed — and is the natural target because it isolates exactly what M-natural-concavity buys on the consumer side alone. Two companion results sharpen the picture: Theorem 11.4 pins down M-natural-concavity through an equivalent single-step ascent property, and Theorem 11.7 restates it through the combinatorial structure (M-natural-convexity) of the demand sets themselves, connecting the definition to the gross-substitutes literature. Proposition 11.12 supplies the structural fact the existence proof turns on (the aggregate excess-cost function inherits M-natural-convexity and has a nonempty subdifferential), Theorem 11.14 extends existence to the general economy with producers by transporting an equilibrium down from a continuous relaxation, and Theorem 11.16 establishes that the set of all equilibrium prices is not merely nonempty but a lattice-structured polyhedron.

Significance

The result itself. Theorem 11.13 is a genuine existence theorem for a discrete general- equilibrium model — not an approximation or a relaxation of the continuous theory, but a free-standing result about the integer lattice. Its consequence set is also constructive by consequence: Theorem 11.16's lattice structure and section 11.5's reduction to submodular-flow computation (not part of this mission) together show that finding an extreme equilibrium price is a polynomial-time problem, not merely a nonempty-existence claim. Before this line of work, economists working with indivisible goods either restricted to special two-sided matching structures or worked with sufficient conditions (such as gross substitutes) whose relationship to each other and to any unifying combinatorial property was not understood; Fujishige–Yang (2003) and Murota–Tamura independently identified the M-natural-concavity connection cited here.

Formalizing it. All of the results in this mission are proved in the source text; nothing here is open. What formalization adds is a machine-checked confirmation that the demand/supply-set and equilibrium definitions, and the exchange-axiom characterization of M-natural-concavity, compose exactly as the informal statements claim — a nontrivial check, since the definitions involve several layers of arg-max/arg-min over integer lattices and price-shifted objectives that are easy to state slightly wrong (e.g. conflating $\arg\max$ and $\arg\min$, or omitting the extended index $0$ in the single-improvement axiom).

Difficulty

The obvious approach to existence — relax the discrete problem to $\mathbb R^K$, apply a classical fixed-point argument, and round the resulting continuous equilibrium to the nearest integer point — fails outright, and the book devotes section 11.2 to a two-agent, two-good example that demonstrates this concretely: at certain initial endowments, every candidate integer allocation leaves an unclaimed unit of surplus, so no equilibrium price exists at all, even though the continuous relaxation of the same economy has one. The gap is a failure of convexity in the Minkowski sum $D_1(p) + D_2(p)$: ordinary discrete demand sets can be "hole-free" individually and still sum to a set with a hole. M-natural-concavity is precisely the condition under which Minkowski sums of demand sets stay hole-free (a consequence of the parallel discrete-convex-set theory this book develops earlier), which is what makes the round-down argument valid after all — but only under this specific hypothesis, not under plain concavity or submodularity.

Formalization scope

Commodities and prices live on a general finite type $K$ (Fintype, DecidableEq), not a fixed $\mathrm{Fin}\ n$; consumers and producers are indexed by general finite types $H$, $L$, with the pure exchange economy realized as the special case $L = \mathrm{PEmpty}$. Utility values lie in WithBot ℝ ($\mathbb R \cup \{-\infty\}$) and cost values in WithTop ℝ ($\mathbb R \cup \{+\infty\}$), matching the book's asymmetric conventions for the two families exactly; no constant appears anywhere in this mission's statements (all hypotheses and conclusions are qualitative), so there is no explicit-constant obligation to record. The one hypothesis that must never be silently dropped is "nondecreasing" in Theorem 11.13: it is a real, separate condition from M-natural-concavity (more of a good is always weakly preferred), stated as its own conjunct rather than folded into the concavity predicate. A formalization that replaced M-natural-concavity with ordinary real-valued concavity, or dropped the boundedness hypothesis on the domains, would be a different — and for indivisible goods, false — statement; section 11.2's example is a concrete witness that discreteness together with a weaker structural hypothesis than M-natural-concavity is not enough. This mission's "M-natural-convex set" (used in Theorem 11.7) and "L-natural-convex polyhedron" (used in Theorem 11.16) are each formalized via one of the book's own stated equivalent characterizations (projection of an M-convex set on an extended ground set, and the (SBS-natural[R]) lattice-translation property respectively) rather than reintroduced as new primitives. Reusable beyond this mission: the general subdifferential SubdiffR and the EReal-valued concave/convex closure constructions apply to any discrete convex/concave function, not only to the aggregate cost function of this chapter. Contributions welcome on the sorry'd proofs, and on formalizing section 11.5's computational reduction to the M-convex submodular flow problem (deferred here — see HARD.md — pending chunk 12's flow vocabulary).

Selected references

• Murota, K. Discrete Convex Analysis. SIAM, 2003. DOI: 10.1137/1.9780898718508. (Chapter 11.)
• Kelso, A. S., Crawford, V. P. "Job Matching, Coalition Formation, and Gross Substitutes." Econometrica 50(6), 1982, 1483–1504.
• Gul, F., Stacchetti, E. "Walrasian Equilibrium with Gross Substitutes." Journal of Economic Theory 87(1), 1999, 95–124.
• Danilov, V., Koshevoy, G., Murota, K. "Discrete Convexity and Equilibria in Economies with Indivisible Goods and Money." Mathematical Social Sciences 41(3), 2001, 251–273.
• Fujishige, S., Yang, Z. "A Note on Kelso and Crawford's Gross Substitutes Condition." Mathematics of Operations Research 28(3), 2003, 463–469.
• Debreu, G. Theory of Value: An Axiomatic Analysis of Economic Equilibrium. Yale University Press, 1959.

Motivation

Minimizing the total weighted completion time $\sum_j w_j C_j$ of jobs with release dates on a single machine, written $1\,|\,r_j\,|\,\sum w_j C_j$ in scheduling notation, is strongly NP-hard. Most constant-factor approximation algorithms for it, and for many related scheduling problems, follow one pattern: solve a linear programming relaxation, which gives a lower bound on the optimum, and round its solution into a schedule whose cost is compared with that bound. The quality of the algorithm is therefore limited by the quality of the relaxation, and the question of which relaxations are equivalent is a basic one for the method.

Two relaxations of $1\,|\,r_j\,|\,\sum w_j C_j$ are central. The time-indexed relaxation of Dyer and Wolsey (doi:10.1016/0166-218X(90)90104-K) has one variable per job and unit time slot, a pseudopolynomial number of variables. The mean busy time relaxation has one variable per job but one constraint per subset of jobs, the shifted parallel inequalities studied by Queyranne and others. Goemans, Queyranne, Schulz, Skutella and Wang (doi:10.1137/S089548019936223X, Section 2) show that both have the same optimal value, and that both are solved by one simple preemptive schedule. This mission formalizes that result, Corollary 2.6, together with the lemmas and theorems its proof uses.

Timeline:

• 1990: Dyer and Wolsey formulate several time-indexed relaxations of $1\,|\,r_j\,|\,\sum w_j C_j$, among them the formulation (D) used here.
• 1993: Queyranne (doi:10.1007/BF01581271) describes the polyhedron of completion-time vectors on one machine without release dates by the parallel inequalities.
• 1994–1995: Queyranne and Schulz use shifted parallel inequalities in polyhedral approaches to machine scheduling with release dates.
• 1996: Goemans (IPCO, LNCS 1084) gives a supermodular relaxation for scheduling with release dates, the source of the canonical decompositions used for (R).
• 2002: Goemans, Queyranne, Schulz, Skutella and Wang prove that (D) and the mean busy time relaxation (R) have equal value, attained by the preemptive "LP schedule", and use this common bound for randomized approximation algorithms with ratios $1.7451$ and $1.6853$.

Setting

There are $n$ jobs $N = \{1, \dots, n\}$. Job $j$ has an integral processing time $p_j > 0$, an integral release date $r_j \ge 0$ and a weight $w_j \ge 0$. For a nonempty set $S$ of jobs, $p(S) = \sum_{j\in S} p_j$ and $r_{\min}(S) = \min_{j \in S} r_j$.

A preemptive schedule gives each job a bounded measurable set $A_j \subseteq [r_j, \infty)$ of processing times of Lebesgue measure $p_j$, the sets pairwise disjoint. The mean busy time of $j$ is $M_j = \frac{1}{p_j}\int_{A_j} t\,dt$.

The LP schedule processes, at every moment, the available (released, unfinished) job of largest ratio $w_j/p_j$, ties broken by index. With the jobs indexed so that $w_1/p_1 \ge \cdots \ge w_n/p_n$, this is the available job of smallest index. As the data are integral, it runs one job or none in each unit slot $[\tau, \tau+1)$; $y^{LP}_{j\tau} \in \{0,1\}$ records whether it runs $j$ there, and $M^{LP}_j$ is its mean busy time of $j$.

The preemptive time-indexed relaxation (D) with horizon $T$ has real variables $y_{j\tau} \ge 0$ for $\tau = r_j, \dots, T-1$ and reads

$$Z_D = \min \sum_j w_j C_j \quad\text{s.t.}\quad \sum_{j : r_j \le \tau} y_{j\tau} \le 1\ (\tau < T), \qquad \sum_{\tau=r_j}^{T-1} y_{j\tau} = p_j, \qquad C_j = \tfrac12 p_j + \tfrac{1}{p_j}\sum_{\tau=r_j}^{T-1}\big(\tau + \tfrac12\big) y_{j\tau}.$$

The mean busy time relaxation (R) reads

$$Z_R = \min \sum_j w_j\big(M_j + \tfrac12 p_j\big) \quad\text{s.t.}\quad \sum_{j\in S} p_j M_j \ge p(S)\big(r_{\min}(S) + \tfrac12 p(S)\big) \quad (\emptyset \ne S \subseteq N).$$

The horizon $T$ is required to bound the makespan of some feasible nonpreemptive schedule, for instance $T = \max_j r_j + \sum_j p_j$.

Formalization targets

Goal: Corollary 2.6

For every instance, every weight vector $w \ge 0$ and every admissible horizon $T$,

$$Z_D = Z_R .$$

No ordering of the jobs and no reference to the LP schedule appear in the goal.

Milestones

1. Lemma 2.1. (D) has an optimal solution with $y_{j\tau} \in \{0,1\}$.
2. Theorem 2.2. With the jobs sorted by $w_j/p_j$, $y^{LP}$ is an optimal solution to (D).
3. Lemma 2.4. For every preemptive schedule and nonempty $S$, $\sum_{j\in S} p_j M_j \ge p(S)\big(r_{\min}(S)+\tfrac12 p(S)\big)$, with equality if and only if $S$ occupies $[r_{\min}(S), r_{\min}(S)+p(S))$ without interruption.
4. Theorem 2.5. With the jobs sorted by $w_j/p_j$, $M^{LP}$ is an optimal solution to (R).
5. Eq. (2.6). $M^{LP}_j = \frac{1}{p_j}\sum_{\tau=r_j}^{T-1} y^{LP}_{j\tau}\big(\tau + \frac12\big)$.

Significance

The equality $Z_D = Z_R$ lets one choose, for each purpose, the more convenient of the two relaxations. (D) is intuitive and a transportation problem, but has pseudopolynomially many variables; (R) has $n$ variables and a supermodular right-hand side, which the paper uses to describe its polyhedron. Theorems 2.2 and 2.5 show that the common optimum is attained by the LP schedule, which is computable greedily; the approximation guarantees of Section 3 of the paper, and of later work on $\alpha$-point scheduling, are all measured against this value.

A formal development adds three things. First, a machine-checked model of preemptive single-machine schedules with release dates, of mean busy times, and of the LP schedule as a concrete recursive object, reusable by any formalization of $\alpha$-point methods (two companion missions of this series use the same objects). Second, a formal statement of the two relaxations with honest optimal values. Third, verified proofs of results that are proved in the paper by short interchange and averaging arguments, whose measure-theoretic details (integrals over processing sets, null sets in the equality case) the paper leaves implicit. The results are proved in the literature; to our knowledge none of them has been machine-checked.

Difficulty

The paper's arguments are short, and each rests on a step that is informal on the page. Lemma 2.1 cites the integrality of transportation problems, a statement about the vertices of a polytope rather than a one-line fact. Theorem 2.2 ends with the claim that a 0/1 solution admitting no improving exchange "must correspond to the LP schedule", which is a property of the greedy rule that has to be derived from its definition. Theorem 2.5 depends on how the LP schedule arranges the jobs of each prefix $\{1, \dots, i\}$ of the sorted order in time; this is the only place sortedness enters, and it is again a property of the concrete schedule. Lemma 2.4 is an extremal statement about integrals over sets of prescribed measure, and its equality case holds only up to null sets. Finally, the goal concerns arbitrary, unsorted weights, while the two theorems it combines are about sorted indices, so the goal is not a direct conjunction of the milestones.

Formalization scope

Jobs are Fin n, numbered from $0$; $p_j$, $r_j$ and the horizon $T$ are natural numbers and weights are real. A preemptive schedule is a family of processing sets $A_j \subseteq \mathbb R$, not indicator functions. The LP schedule is defined by recursion on unit slots with the smallest-index rule; the sortedness of $w_j/p_j$ is a hypothesis of Theorems 2.2 and 2.5, not part of the definition. Variables of (D) are functions $y : \text{jobs} \times \mathbb N \to \mathbb R$ required to vanish outside $r_j \le \tau < T$. $Z_D$ and $Z_R$ are infima of the objective over the feasible sets; under the stated hypotheses the feasible sets are nonempty and the objectives bounded below, so these are the LP values. Optimality in the milestones is stated as attaining the minimum, not through these infima.

The horizon hypothesis rules out the trivializing case in which (D) is infeasible and its infimum takes the junk value $0$; (D) is kept a linear program over real $y$, since restricting to $\{0,1\}$ would make Lemma 2.1 vacuous. The running-time claim of Corollary 2.6 ($O(n\log n)$) is not formalized.

A complete development needs: integrals of the identity over finite unions of intervals; a rearrangement lemma for sets of given measure; basic properties of the LP schedule (it is a preemptive schedule, it is work-conserving and finishes by any admissible $T$, its blocks are canonical); and a relabelling argument. The schedule model and the LP-schedule lemmas are reusable for the companion missions on $\alpha$-point scheduling. Contributions of any of these lemmas as separate theorems are welcome.

Selected references

• M. X. Goemans, M. Queyranne, A. S. Schulz, M. Skutella, Y. Wang, Single machine scheduling with release dates, SIAM Journal on Discrete Mathematics 15(2):165–192, 2002. doi:10.1137/S089548019936223X
• M. E. Dyer, L. A. Wolsey, Formulating the single machine sequencing problem with release dates as a mixed integer program, Discrete Applied Mathematics 26(2–3):255–270, 1990. doi:10.1016/0166-218X(90)90104-K
• M. Queyranne, Structure of a simple scheduling polyhedron, Mathematical Programming 58:263–285, 1993. doi:10.1007/BF01581271
• M. X. Goemans, Improved approximation algorithms for scheduling with release dates, Proceedings of the 8th ACM-SIAM Symposium on Discrete Algorithms (SODA), 591–598, 1997.

Motivation

Chapter 10 turns from structure theory to algorithms: efficient methods for minimizing M-convex functions (via domain reduction) and submodular set functions (via Schrijver's and the Iwata-Fleischer-Fujishige scaling algorithms). Most of chapter 10's numbered results are asymptotic running-time bounds for specific procedural algorithms — a genuinely different kind of claim from the rest of this book (see Formalization scope). This mission places the results of this block that ARE ordinary mathematical propositions: correctness certificates, min-max theorems, and structural facts the algorithms rely on and produce.

Setting

For an M-convex set B ⊆ Z^V, the central part B° (the vectors of B lying away from its boundary, defined via per-coordinate bounds ℓ°_B, u°_B) is what the domain reduction algorithm searches from. For a submodular set function ρ : 2^V → R, the base polyhedron B(ρ) and its extreme bases (one per linear ordering of V, via Eq. (10.12)) let any base be written as a convex combination of finitely many extreme bases (Eq. (10.13)); a candidate minimizer W is certified via the linear orderings representing an optimal base. The Iwata-Fleischer-Fujishige (IFF) scaling algorithm relaxes this problem with a flow-augmentation parameter δ, maintaining a δ-feasible flow φ and vector z = x + ∂φ; near the end of a scaling phase, no augmenting path and no "active triple" together certify near-optimality.

Formalization targets

Goal: Near-optimality from the absence of augmenting paths (Proposition 10.20)

If S ⊆ W ⊆ V∖T, no arc of the auxiliary network leaves W, and no active triple exists, then z⁻(V) ≥ ρ(W)-nδ and x⁻(V) ≥ ρ(W)-n²δ; moreover W exactly minimizes ρ once δ is small enough relative to the smallest positive gap between two values of ρ. Chosen as goal: the book calls this "a key property of the scaling algorithm" and "a relaxation version of the min-max relation in Proposition 10.8", its own proof is the most substantial argument among this chunk's placed results, and Proposition 10.23 is a direct corollary of it.

Supporting structural targets

Proposition 10.8 is the min-max relation underlying the whole of section 10.2 (an Edmonds- intersection-theorem consequence, found by direct reading — the extractor's table missed it). Proposition 10.9 gives the three-part sufficient condition for optimality, in terms of the linear orderings representing an optimal base, that both Schrijver's algorithm and the IFF algorithm use as their termination criterion (also found by direct reading). Propositions 10.5-10.6 establish that the domain reduction algorithm's central part B° is always nonempty, via an explicit vector-extension step (Proposition 10.5 likewise missing from the extractor's table). Proposition 10.23 fixes individual coordinates once a scaling phase ends, and Proposition 10.24 gives the termination certificate for the IFF fixing algorithm's own separate graph-contraction procedure.

Significance

Chapter 10 is where this book cashes out its structure theory as algorithms with provable running times, and this mission places every result of that chapter's first two sections that is a mathematical proposition rather than a runtime bound: two min-max/optimality-certificate theorems (10.8-10.9) that are the combinatorial core making the following two strongly polynomial algorithms (Schrijver's, and Iwata-Fleischer-Fujishige's) correct, one central-part nonemptiness fact (10.5-10.6) underlying the domain reduction algorithm, and the two fixing/termination certificates (10.23-10.24) that let the scaling algorithms actually output a minimizer with a proof of optimality attached, not just a numerical answer.

None of these results are open — they are Murota's own account of submodular-function- minimization algorithms (sections 10.1-10.2). What this mission contributes is a faithful, machine-checked formal statement of each, including two results (Propositions 10.5 and 10.8) the platform's own automated extractor missed; no comparable formalization exists on the platform (see Formalization scope).

Difficulty

Six numbered results in this block (Propositions 10.4, 10.7, 10.17, 10.18, 10.21, 10.22) are excluded as hard: each states a Big-O asymptotic bound on the running time, function-evaluation count, or internal-procedure-call count of a specific iterative algorithm (the domain reduction algorithm, its scaling variant, Schrijver's algorithm, the IFF scaling algorithm). Faithfully stating "this algorithm runs in O(g(n)) time" requires a cost-tracked operational semantics for that specific algorithm — a well-founded recursive procedure with an oracle for evaluating the input function, threading a step/evaluation counter, instantiated over an unbounded family of ground-set sizes n and numeric parameters (K∞, M) — which is a fundamentally different kind of formalization task (computational complexity theory) from every one of the roughly 280 other numbered results in this book, none of which require modeling the cost of computing them. See HARD.md.

Formalization scope

Ground-set elements are a Fintype V with DecidableEq. Base M-convex-set vocabulary is redeclared from prior missions. Linear orderings of V are represented as bijections V ≃ Fin (Fintype.card V) rather than as lists, matching this series' established preference for order-indexed families over sequential data structures. Proposition 10.5's witness vector is stated as an existence claim (the mathematical content of the proposition), rather than by reconstructing the specific recursive modification procedure the book uses to produce it — a choice consistent with how this series has always formalized "the algorithm produces X" claims where X is a mathematical property, by asserting X's existence rather than executing the algorithm (see, e.g., mission 33-ch09c-networkflows's cycle-cancellation theorem). Six numbered results (Propositions 10.4, 10.7, 10.17, 10.18, 10.21, 10.22) are hard; see HARD.md. Contributions completing any of the seven sorrys are welcome; the goal and Proposition 10.9 carry the most independent proof content.

Selected references

• K. Murota, Discrete Convex Analysis, SIAM, 2003. DOI: 10.1137/1.9780898718508.
• S. Iwata, L. Fleischer, and S. Fujishige, "A combinatorial strongly polynomial algorithm for minimizing submodular functions," Journal of the ACM, 48 (2001), pp. 761-777 [102] (the IFF scaling algorithm this mission's Proposition 10.20 certifies).
• A. Schrijver, "A combinatorial algorithm minimizing submodular functions in strongly polynomial time," Journal of Combinatorial Theory, Series B, 80 (2000), pp. 346-355 [182] (Schrijver's algorithm, whose termination criterion is Proposition 10.9).

Motivation

Chapters 6 through 9 characterized minimality for M-convex functions structurally (the M-optimality criterion, Theorem 6.26: a point is a global minimizer iff no local swap improves it) without saying how to find one. Chapter 10 turns that structural fact into an algorithm: the local characterization is the termination test of the simplest possible minimization procedure, steepest descent by coordinate swaps. This mission formalizes that algorithm and its two complexity bounds, plus a structural min-max identity for submodular base polyhedra that the chapter's heavier submodular-minimization algorithms build on. It is the first mission in this series whose goal names a method, not just a property of a class of functions — formalizing it faithfully means giving the algorithm itself a Lean representation that the complexity theorem then quantifies over, not just describing its output.

Setting

Let $f : \mathbb Z^V \to \mathbb R \cup \{+\infty\}$ be an M-convex function (MExchangeAxiom, chunk 06), $n = |V|$. The steepest descent algorithm repeatedly replaces the current point $x$ by $x - \chi_u + \chi_v$ for a pair $u \ne v$ minimizing $f(x - \chi_u + \chi_v)$, stopping when no such swap improves on $f(x)$ — at which point, by the M-optimality criterion, $x$ is a global minimizer. This mission represents a run of the algorithm as a sequence $x : \mathbb N \to \mathbb Z^V$ satisfying these step and termination relations directly, so that "the number of iterations" is a genuine property of any such run, not an informal gloss. Separately, for a submodular set function $\rho : 2^V \to \mathbb R$ (chunk 04's SubmodularSetFunction), the base polyhedron $B(\rho)$ (chunk 04's BasePolyhedron) is the polytope $\{x \in \mathbb R^V : x(X) \le \rho(X)\ \forall X,\ x(V) = \rho(V)\}$.

Formalization targets

Goal: Proposition 10.2 (iteration bound with tie-breaking)

For an M-convex function $f$ with finite $\ell^1$-diameter $K_1 = \max\{\|x-y\|_1 : x,y \in \operatorname{dom} f\}$ (Eq. (10.1)), the number of iterations in the steepest descent algorithm using the tie-breaking rule (10.2) — a fixed lexicographic rule for choosing among tied steepest pairs, based on an arbitrary but fixed ordering $\varphi$ of $V$ — is bounded by $K_1/2$.

Milestones: Proposition 10.1, Proposition 10.8

Proposition 10.1: an unconditional warm-up — if $f$ has a unique minimizer $x^*$, any run of the plain (untied) algorithm from $x^0$ terminates within $\|x^0 - x^*\|_1/2$ iterations, with no tie-breaking rule needed. Proposition 10.8: a structural min-max identity, $\max\{x^-(V) : x \in B(\rho)\} = \min\{\rho(X) : X \subseteq V\}$, for a submodular set function $\rho$ — chosen deliberately as a milestone that names no algorithm at all, in contrast to this mission's other two items.

Significance

The result itself. Proposition 10.2 is the complexity backbone of §10.1: it is what turns "steepest descent terminates" (an easy monotonicity observation) into a genuine polynomial bound, and it is the base case the chapter's more elaborate scaling and domain-reduction algorithms (not drafted here) improve on. Proposition 10.8, though algorithm-free, is the structural fact ("verifying membership in $B(\rho)$ seems to need a submodular minimization procedure — but demonstrating optimality of a cut $X$ only needs a base with $x^-(V) = \rho(X)$") that makes Schrijver's and the IFF algorithms' correctness proofs possible, and specializes chunk 04's Edmonds's intersection theorem to the two-function case $\rho_1 = \rho, \rho_2 = 0$.

Formalizing it. A prior-art search (q=steepest descent, q=submodular minimization, q=base polyhedron) found no existing platform items for any of this chapter's results. This mission gives the first formal statement of an M-convex minimization algorithm's complexity, and directly reuses chunk 06's MExchangeAxiom/ArgMin/CharVec/DomZ and chunk 04's SubmodularSetFunction/BasePolyhedron — genuine cross-chunk substrate reuse spanning two different chapters' worth of prior missions.

Difficulty

The chapter's own framing (quoted in BRIEF.md) is that every other chapter's theorems state a property of a class of functions or sets, while this chapter's theorems state properties of a named algorithm run on such an object. Formalizing "the number of iterations in the steepest descent algorithm is bounded by ..." faithfully means giving the algorithm's steps (S0-S3) and termination test a Lean representation that the bound then quantifies over — stating the bound about "the minimizer" alone, with the algorithm silently dropped, would misrepresent the theorem as a fact about minimizers rather than about a procedure that finds one. This mission represents a run of the algorithm as an abstract sequence satisfying the book's own step-transition relations (documented in full in MODERATION_NOTES.md), letting the complexity theorems quantify over any valid run rather than committing to one executable implementation.

A second difficulty is scope: the chapter's recommended primary goal, Proposition 10.18 (Schrijver's algorithm's complexity), needs a scaling procedure with several auxiliary data structures — a materially larger definitional undertaking than steepest descent's simple greedy-swap loop. Per BRIEF.md's own explicit fallback authorization, this mission takes Proposition 10.2 as its goal instead; see HARD.md and MODERATION_NOTES.md for the full reasoning.

Formalization scope

Runs of the algorithm are represented as x : ℕ → V → ℤ satisfying IsSteepestDescentRun (plain) or IsSteepestDescentRunTieBreak (with the tie-breaking rule) — a step relation plus a termination test, with an explicit iteration count N the theorems bound. The tie-breaking key $\Phi(u,v)$ (Eq. (10.2)) and its lexicographic order are formalized directly (a manual three-way comparison, not Mathlib's default componentwise Prod order). Both iteration bounds are stated as 2 * N ≤ k rather than N ≤ k / 2, avoiding natural-number division. Not drafted: the derived "hence ... in $O(F \cdot n^2 K_1)$ time" corollary of Proposition 10.2 (needs a cost-model primitive for "time" and "$F$" this mission does not otherwise use — the iteration-count bound itself, this proposition's genuine combinatorial content, is drafted in full); Schrijver's algorithm and everything in §10.2.2 onward; the steepest descent scaling algorithm, the domain reduction algorithm and its scaling variant (§10.1.2-10.1.3, structurally different algorithms); the quasi-M-convex extension mentioned immediately after Proposition 10.2. A trivializing formalization would state the iteration bound as an unconditional fact about "a" minimizer-finding procedure, or would drop the tie-breaking rule from Proposition 10.2 and thereby understate what the bound actually requires; neither is done.

Selected references

• K. Murota, Discrete Convex Analysis, SIAM, 2003. DOI: 10.1137/1.9780898718508.

Motivation

Mission 32-ch09b-networkflows established the potential and negative-cycle optimality criteria for M-convex submodular flow problems. This mission finishes chapter 9 with the two topics that close it out: the constructive engine behind the negative-cycle criterion — cycle cancellation, which actually improves a nonoptimal flow rather than merely detecting suboptimality, resting on a delicate "unique-min condition" for bipartite matchings — and network duality, the chapter's capstone structural theorem showing that M-convexity and L-convexity are preserved (and their conjugacy is preserved) under transformation by an arbitrary network.

Setting

For a feasible integer flow ξ in the M-convex submodular flow problem MSFP2, a negative cycle in the auxiliary network (Gξ,ℓξ) witnesses suboptimality (mission 32's Theorem 9.20); cycle cancellation modifies ξ along a smallest such cycle to produce a strictly better flow ξ̄ (Eq. (9.75)). The unique-min condition for a pair (x,y) of integer vectors with ‖x-y‖∞=1 asks whether the bipartite graph G(x,y) — vertices the positive/negative supports of x-y, weights the M-convex exchange values Δf(x;v,u) — has a unique minimum-weight perfect matching; when it does, the M-convex exchange inequality of Proposition 6.25 becomes an equality. Separately, a network G=(V,A;S,T) with entrance set S and exit set T transforms a pair of functions f,g on Zˢ into induced functions f̃,g̃ on Zᵀ (Eqs. (9.81)-(9.82)), the minimum cost to meet a boundary specification at the exit given a production cost at the entrance and a transportation cost along arcs.

Formalization targets

Goal: Network duality for Z→Z functions (Theorem 9.26)

M-(resp. M$^\natural$-)convexity and integer-valuedness of f transfer to the induced f̃; L-(resp. L$^\natural$-)convexity and integer-valuedness of g transfer to g̃; and if f is M$^\natural$-convex, g is its L$^\natural$-conjugate, and each arc cost ga is the conjugate of fa, then g̃ is the conjugate of f̃. Chosen as goal: the book calls this "the harmonious relationship between network flow and M-/L-convexity", its own proof runs roughly six pages (the longest argument in this chunk), and it is the general fact from which Theorems 9.27-9.28 (analogues for other type combinations) and Notes 9.29-9.30 (the aggregation and infimal- convolution closure properties of M-convex functions, already placed in mission 22-ch06b-mconvexfunctions's own Theorem 6.13) all descend.

Supporting structural targets

Theorem 9.22 shows cycle cancellation strictly improves the objective; Propositions 9.23-9.25 are "the key ingredient" behind it: Proposition 9.23 shows the unique-min condition upgrades the M-convex exchange inequality to an equality, Proposition 9.24 gives a checkable characterization of when a bipartite weighted graph has a unique minimum-weight perfect matching, and Proposition 9.25 is the fact that makes the machine run — the specific pair (∂ξ,∂ξ̄) arising from cycle cancellation always satisfies the unique-min condition. Theorems 9.27 and 9.28 are the network duality theorem's own analogues for Z→R and R→R functions, the second restoring the conjugacy assertion (missing for Z→R) via the ordinary real Legendre-Fenchel transform.

Significance

Cycle cancellation is this book's constructive answer to the negative-cycle criterion: not just a certificate of suboptimality, but an actual improvement step, the combinatorial core of the cycle-canceling algorithm explained in section 10.4.3 (mission 35-ch10c-algorithms). Its correctness proof is one of the most intricate combinatorial arguments in the entire book — a proof by contradiction using a multiset-union identity (Eq. (9.80)) to derive a smaller negative cycle from an assumed non-uniqueness, itself resting on Proposition 9.24's Monge-like characterization of unique bipartite matchings. Network duality, meanwhile, is the theorem that explains why discrete convex analysis and network flow theory are so tightly intertwined throughout this book: it is the general mechanism (matroid induction, min-max relations, the M-convex aggregation and infimal-convolution closure properties) underlying nearly every construction chapter 2 introduced informally and chapter 6 proved piecemeal.

None of these results are open — they are Murota's own account of cycle cancellation (section 9.5.2) and network duality (section 9.6). What this mission contributes is a faithful, machine-checked formal statement of each, completing the platform's coverage of chapter 9 begun in missions 12-network-flows and 32-ch09b-networkflows; no comparable formalization exists on the platform (see Formalization scope).

Difficulty

Theorem 9.26's own proof needs the full weight of everything chapter 9 has built (Theorem 9.16's potential criterion for integer flows, the conjugacy theorem of chapter 8), which this mission does not re-prove (proofs are sorry throughout, per this pass's scope) but whose statement still needs the induced-function machinery built faithfully: since the general framework's optimal-value-type quantities can genuinely be -∞ (the book's own blanket hypothesis f̃ > -∞ acknowledges this), InducedFTilde/InducedGTilde are EReal-valued, following the same soundness discipline established in mission 31-ch08d-conjugacyduality for Lagrangian duality's derived quantities.

Formalization scope

Ground-set vertices V and arcs A are Fintype with DecidableEq. All base M-/L-convexity vocabulary is redeclared from prior missions. Functions "on Zˢ" for S a proper subset of the ground set are represented as ordinary V→Z functions required to vanish outside S (SupportedOn), rather than as functions on a dependent subset type — a padding-with-zero encoding consistent with this whole series' preference for a single ambient ground-set domain. C[R→R] (univariate real polyhedral convex functions, needed only for Theorem 9.28's arc costs) is formalized as ordinary midpoint-style convexity (IsConvexUnivariateR) rather than the book's own polyhedral characterization, since polyhedrality plays no role in Theorem 9.28's conclusion beyond ensuring the induced functions are well-behaved. All seven numbered results found in this chunk's page range are placed in full, with no partial-coverage scope reduction. Contributions completing any of the seven sorrys are welcome; the goal and Proposition 9.25 carry the most independent proof content.

Selected references

• K. Murota, Discrete Convex Analysis, SIAM, 2003. DOI: 10.1137/1.9780898718508.
• K. Murota, "Valuated matroid intersection," SIAM Journal on Discrete Mathematics, 9 (1996), pp. 545-561 [135] (the unique-max lemma Proposition 9.23 reformulates, and the proof technique behind Proposition 9.25).
• K. Murota, "Discrete convex analysis," Mathematical Programming, 83 (1998), pp. 313-371 [140] (network duality and cycle cancellation for the M-convex submodular flow problem).

Motivation

Chapter 9 is where discrete convex analysis meets classical network flow theory: the minimum cost flow problem's three hallmark properties — an optimality criterion by potentials, an optimality criterion by negative cycles, and integrality of optimal solutions — are shown to survive, in a precise and increasingly general form, first for arbitrary polyhedral convex costs (MCFP3), then for the M-convex submodular flow problem (MSFP2/MSFP3), the chapter's own combinatorial generalization of the classical problem. This mission places the potential criterion (Theorem 9.4) and its cascade of six corollaries and generalizations, the block of results this book's own text uses to carry every other result in the chapter.

Setting

A digraph G = (V,A) with tail/head maps ∂⁺,∂⁻ : A → V. A flow ξ : A → R has boundary ∂ξ(v) = Σ{ξ(a) : ∂⁺a=v} − Σ{ξ(a) : ∂⁻a=v}. A potential p : V → R has coboundary δp(a) = p(∂⁺a) − p(∂⁻a). The minimum cost flow problem MCFP3 minimizes Γ₃(ξ) = Σₐ fₐ(ξ(a)) + f(∂ξ) over flows, for polyhedral convex arc costs fₐ : R → R∪{+∞} and boundary cost f : Rⱽ → R∪{+∞}; MCFP0 is its linear-cost, fixed-supply special case. The M-convex submodular flow problem MSFP3 is MCFP3 with f additionally M-convex; MSFP2 is its linear-arc-cost special case.

Formalization targets

Goal: The potential criterion for MCFP3 (Theorem 9.4)

For a feasible flow ξ, ξ is optimal for MCFP3 iff there is a potential p with ξ(a) a minimizer of the reduced arc cost fₐ[δp(a)] for every arc and ∂ξ a minimizer of the reduced boundary cost f[−p]; and any such optimal potential characterizes optimality of every feasible flow. This is the hub result of the whole chunk: the book states Theorem 9.14 is "immediate" from it, and every other placed result either specializes it directly or builds on that specialization.

Supporting structural targets

Theorem 9.5 reformulates MCFP0's optimality as the absence of a negative cycle in an auxiliary network; Theorem 9.6 gives MCFP0's primal and dual integrality, the latter identifying the optimal-potential set as an L-convex polyhedron. Theorem 9.14 specializes the goal to MSFP3; Theorem 9.15 upgrades this to a full polyhedral and integrality structure theorem for MSFP3's optimal-flow-boundary and optimal-potential sets (M2-convex and L-convex polyhedra respectively); Theorem 9.16 is the integer-flow analogue, with the boundary set now literally M2-convex and the integer-optimal-potential set literally L-convex. Theorems 9.18 and 9.20 give the negative-cycle reformulation for MSFP2, real and integer flows respectively, generalizing Theorem 9.5 by admitting a third class of auxiliary arcs governed by the M-convex boundary cost's directional derivative (or its discrete difference, in the integer case).

Significance

This is the chapter's demonstration that M-convexity is not merely an abstract combinatorial axiom but the exact structural hypothesis under which classical network-flow duality survives intact: every one of the four "nice properties" the book opens the chapter with (potentials, negative cycles, integrality, efficient algorithms) is preserved verbatim in the M-convex generalization, and this mission's eight results are the proof of that claim for the first three. The chunk's own internal dependency structure — one foundational theorem (9.4) from which every other placed result descends by specialization or direct generalization — is itself characteristic of how this book organizes its combinatorial machinery around a single convex- analytic core.

None of these results are open — they are Murota's own account of network flow duality under M-convexity (sections 9.1, 9.4, and 9.5). What this mission contributes is a faithful, machine-checked formal statement of each, extending the platform's coverage of chapter 9 begun in mission 12-network-flows (which covered §9.1.1-9.1.2 and §9.3, the feasibility and max-flow min-cut results, deliberately leaving this block for later apparatus); no comparable formalization exists on the platform (see Formalization scope).

Difficulty

The eight results span real- and integer-flow versions of two nested problem hierarchies (MCFP0 ⊂ MCFP3, MSFP2 ⊂ MSFP3) and two distinct optimality certificates (potentials, negative cycles), which this mission handles by building one shared apparatus — FeasibleFlowMCFP3, Gamma3, OptimalFlowMCFP3, IsOptimalPotential — that MCFP0 and MSFP3 both instantiate (MCFP0 literally as the linear-cost/singleton-boundary special case of Eq. (9.11)), and one shared generic cycle/negative-cycle apparatus (IsCycle, CycleLength, HasNegativeCycle) instantiated three times with different auxiliary-arc types (A⊕A for MCFP0, A⊕A⊕(V×V) for MSFP2's extra Cξ arcs governed by the boundary cost's directional derivative). "Primal integral" and "dual integral" polyhedral convex functions (the book's own C[Z|R→R]/C[R→R|Z] notation, used in Theorem 9.15) needed a modeling decision, since the book's own definition of these classes lies outside this chunk's page range; see Formalization scope.

Formalization scope

Ground-set vertices V and arcs A are Fintype with DecidableEq. All base M-/L-convexity vocabulary is redeclared from prior missions in this series. "Primal integral" (C[Z|R→R], M[Z|R→R]) is formalized as integer effective domain (IsDomainIntegerArc/IsDomainIntegerR); "dual integral" (C[R→R|Z], M[R→R|Z]) is formalized as the existence of an integer subgradient at every domain point (IsDualIntegralArc/IsDualIntegralR) — a standard equivalent characterization for polyhedral convex functions, and a deliberate modeling choice recorded in MODERATION_NOTES.md rather than a literal transcription of the book's own (out-of-range) definition of these two notation classes. All eight numbered results found in this chunk's page range are placed in full, with no partial-coverage scope reduction. Contributions completing any of the eight sorrys are welcome; the goal and Theorem 9.15 carry the most independent proof content.

Selected references

• K. Murota, Discrete Convex Analysis, SIAM, 2003. DOI: 10.1137/1.9780898718508.
• R. T. Rockafellar, Network Flows and Monotropic Optimization, Wiley, 1984 [178] (the classical potential/Fenchel-duality framework this mission's Theorem 9.4 adapts).
• K. Murota, "Discrete convex analysis," Mathematical Programming, 83 (1998), pp. 313-371 [140] (the Lagrange duality and negative-cycle theory of section 9.5 this mission's Theorems 9.18 and 9.20 draw from).

Motivation

In best arm identification with fixed confidence, a learner samples $K$ unknown distributions ("arms") sequentially and must name the arm with the largest mean, with error probability at most a prescribed $\delta$, using as few samples as possible. Garivier and Kaufmann (arXiv:1602.04589, COLT 2016) showed that every $\delta$-PAC strategy needs, in expectation, at least $T^*(\boldsymbol\mu)\,\mathrm{kl}(\delta,1-\delta)$ samples, where the characteristic time $T^*(\boldsymbol\mu)$ is the value of a max–min optimization problem over the proportions of draws allocated to the arms. The maximizer of that problem, $w^*(\boldsymbol\mu)$, is the allocation any asymptotically optimal strategy must follow; the Track-and-Stop algorithm of the same paper computes $w^*(\hat{\boldsymbol\mu})$ at plug-in estimates and tracks it.

A strategy can only track $w^*$ if $w^*$ can be computed. This mission formalizes the part of the paper (Section 2.2 and Appendix A) that turns the abstract max–min problem into an explicit recipe: a closed form for the inner infimum, and a characterization of $w^*$ through the root of one increasing scalar function. The problem had been solved in closed form before only for special cases, such as Poisson rewards with all suboptimal arms equal (Vaidhyan and Sundaresan, 2015); the paper's result covers every one-parameter exponential family.

Setting

A canonical one-parameter exponential family is a family of laws $\nu_\theta$, $\theta\in\Theta$, on $\mathbb R$ with density $\exp(\theta x-b(\theta))$ with respect to a reference measure $\xi$. The law $\nu_\theta$ has mean $\dot b(\theta)$; the set of attainable means is the mean space $\dot b(\Theta)$. For means $\mu=\dot b(\theta)$ and $\mu'=\dot b(\theta')$ the Kullback–Leibler divergence is

$$d(\mu,\mu')=\mathrm{KL}(\nu_\theta,\nu_{\theta'})=b(\theta')-b(\theta)-\dot b(\theta)(\theta'-\theta).$$

Bernoulli laws and Gaussian laws with known variance are the standard examples.

A bandit model is identified with its vector of means $\boldsymbol\mu=(\mu_1,\dots,\mu_K)\in\dot b(\Theta)^K$. $\mathcal S$ is the set of models with a unique optimal arm $a^*(\boldsymbol\mu)$, and $\mathrm{Alt}(\boldsymbol\mu)=\{\boldsymbol\lambda\in\mathcal S:a^*(\boldsymbol\lambda)\ne a^*(\boldsymbol\mu)\}$ is the set of alternatives. $\Sigma_K$ is the probability simplex. The transportation cost of proportions $w\in\Sigma_K$ and the objects of the paper are

$$c_{\boldsymbol\mu}(w)=\inf_{\boldsymbol\lambda\in\mathrm{Alt}(\boldsymbol\mu)}\sum_{a=1}^Kw_a\,d(\mu_a,\lambda_a),\qquad T^*(\boldsymbol\mu)^{-1}=\sup_{w\in\Sigma_K}c_{\boldsymbol\mu}(w),\qquad w^*(\boldsymbol\mu)=\operatorname*{argmax}_{w\in\Sigma_K}c_{\boldsymbol\mu}(w).$$

The arms are sorted so that $\mu_1>\mu_2\ge\dots\ge\mu_K$. The parameterized Jensen–Shannon divergence is, for $\alpha\in[0,1]$,

$$I_\alpha(\mu_1,\mu_2)=\alpha\,d\big(\mu_1,\alpha\mu_1+(1-\alpha)\mu_2\big)+(1-\alpha)\,d\big(\mu_2,\alpha\mu_1+(1-\alpha)\mu_2\big).$$

For $a\in\{2,\dots,K\}$ let $g_a(x)=(1+x)I_{1/(1+x)}(\mu_1,\mu_a)$ for $x\ge0$, let $x_a=g_a^{-1}$, and let $x_1\equiv1$. Finally

$$F_{\boldsymbol\mu}(y)=\sum_{a=2}^K\frac{d\big(\mu_1,m_a(y)\big)}{d\big(\mu_a,m_a(y)\big)},\qquad m_a(y)=\frac{\mu_1+x_a(y)\mu_a}{1+x_a(y)}.$$

Formalization targets

Goal: Theorem 5 (p. 5)

With $D=d(\mu_1,\mu_2)$: $F_{\boldsymbol\mu}$ is continuous and strictly increasing on $[0,D[$, $F_{\boldsymbol\mu}(0)=0$, $F_{\boldsymbol\mu}(y)\to\infty$ as $y\to D$, the equation $F_{\boldsymbol\mu}(y)=1$ has a unique solution $y^*\in[0,D[$, and

$$w\in w^*(\boldsymbol\mu)\iff w_a=\frac{x_a(y^*)}{\sum_{i=1}^Kx_i(y^*)}\quad\text{for every arm }a.$$

The equivalence says at once that the argmax exists, that it is a single point, and that it is given by eq. (5).

Milestones

1. Lemma 3 (p. 5): for every $w\in\Sigma_K$,

$$c_{\boldsymbol\mu}(w)=\min_{a\ne1}(w_1+w_a)\,I_{\frac{w_1}{w_1+w_a}}(\mu_1,\mu_a).$$

2. Claim after eq. (4) (p. 5): $g_a$ is a strictly increasing one-to-one mapping from $[0,+\infty[$ onto $[0,d(\mu_1,\mu_a)[$.
3. Lemma 4 (p. 5): for every maximizer $w^*$ and all $a,b\in\{2,\dots,K\}$,

$$(w^*_1+w^*_a)I_{\frac{w^*_1}{w^*_1+w^*_a}}(\mu_1,\mu_a)=(w^*_1+w^*_b)I_{\frac{w^*_1}{w^*_1+w^*_b}}(\mu_1,\mu_b).$$

Significance

The result. Theorem 5 reduces a $(K-1)$-dimensional non-smooth max–min problem to finding the root of one continuous increasing function on a bounded interval, each evaluation of which requires $K-1$ scalar inversions. It gives existence and uniqueness of $w^*(\boldsymbol\mu)$, which the paper's lower bound only presupposes, and it is the computational core of Track-and-Stop: without an explicit, well-posed $w^*$ the tracking strategy is not defined. Lemma 3 alone gives the closed form of the inner infimum used again in the analysis of the stopping rule.

Formalizing it. The results are proved in the paper; nothing here is open. To the best of our knowledge none of them has a machine-checked proof: the platform's existing best-arm-identification rows concern Gaussian arms and state the characteristic time at the level of measures, without this characterization. The mission produces a checked reduction for general one-parameter exponential families, including the edge cases the text passes over (ties among suboptimal arms, zero weights, the behaviour of $x_a$ near the end of its domain).

Difficulty

The infimum in Lemma 3 ranges over $\mathrm{Alt}(\boldsymbol\mu)$, a set of models with a unique best arm, so it is an open condition: the minimizing configuration, in which $\lambda_1$ and $\lambda_a$ coincide, lies outside $\mathrm{Alt}(\boldsymbol\mu)$ and is only approached. Other arms may also compete for the best position. A statement in which the infimum is taken over the closed relaxation $\{\lambda_a\ge\lambda_1\}$ is a lemma of the proof, not Lemma 3.

For Theorem 5, the equalization in Lemma 4 needs an argument that holds for every maximizer, not only for one found by a first-order condition, because the objective is a minimum of functions and is not differentiable. The monotonicity of $F_{\boldsymbol\mu}$ needs the monotonicity of each $x_a$ and of each ratio in the moving point $m_a$, and the limit at $D$ rests on the second-best arm(s) only, which is where the ordering $\mu_1>\mu_2\ge\dots$ enters. Finally the analytic facts about $d$ (continuity, positivity off the diagonal, monotonicity in each argument) must be derived from the exponential family itself.

Formalization scope

Lean represents a model by μ : Fin K → ℝ with 2 ≤ K and every μ a in the mean space deriv F.b '' F.Θ. The paper's arm $1$ is index 0, arm $2$ is index 1. The exponential family is a structure ExpFamily whose parameter set is a nonempty open interval and whose b is twice continuously differentiable with $\ddot b>0$ on $\Theta$. These two conditions are added to the paper's "convex, twice differentiable" and are disclosed: strict convexity is what makes the mean parameterization unique, and openness is what lets alternatives approach the boundary of $\mathrm{Alt}(\boldsymbol\mu)$. The reference measure and normalization are part of the structure but unused here.

The transportation cost is an infimum in EReal, so it is the true infimum of the set of values rather than a default 0. $w^*(\boldsymbol\mu)$ is never defined by choice: "$w$ is optimal" is a predicate, and Theorem 5 characterizes the set of such $w$. The functions $x_a$ are the inverse of $g_a$ on $[0,+\infty[$ and are evaluated only on $[0,d(\mu_1,\mu_2)[$. "Increasing" in Theorem 5 is stated as strictly increasing, as proved in Appendix A.2. Lemmas 3 and 4 and the claim on $g_a$ assume only that arm $1$ is the unique best arm, which is weaker than the paper's standing ordering.

A formalization that replaces $\mathrm{Alt}(\boldsymbol\mu)$ by $\{\lambda_a\ge\lambda_1\}$, assumes the maximizer exists and is unique, or asserts only existence of some $y^*$ without the formula for $w^*$, does not state these results and is ruled out.

A complete development needs: basic calculus of exponential families (the Bregman form of $d$, its continuity and strict positivity off the diagonal, its monotonicity in the second argument), the inverse function of a continuous strictly monotone map on an interval, and compactness of the simplex. The divergence facts are reusable in every bandit mission built on exponential families. Proofs of the milestones, of auxiliary facts about $d$ and $I_\alpha$, and a sorry-free instance of ExpFamily (Bernoulli or unit-variance Gaussian) are welcome.

Selected references

• A. Garivier, E. Kaufmann, Optimal Best Arm Identification with Fixed Confidence, COLT 2016 (JMLR W&CP 49), arXiv:1602.04589v2. https://arxiv.org/abs/1602.04589
• E. Kaufmann, O. Cappé, A. Garivier, On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models, JMLR 17, 2016. https://arxiv.org/abs/1407.4443
• N. K. Vaidhyan, R. Sundaresan, Learning to detect an oddball target, arXiv:1508.05572, 2015. https://arxiv.org/abs/1508.05572
• O. Cappé, A. Garivier, O.-A. Maillard, R. Munos, G. Stoltz, Kullback–Leibler upper confidence bounds for optimal sequential allocation, Annals of Statistics 41(3), 2013. https://arxiv.org/abs/1210.1136

Motivation

A false failure return is a product returned by a consumer as defective although it has no functional or cosmetic defect; managers attribute such returns to installation difficulties, a mismatch with the consumer's preferences, and remorse. Ferguson, Guide and Souza report (pp. 376–377) that false failures account for up to 80% of Hewlett-Packard's inkjet printer returns, roughly 5% of sales, and that the per-unit cost of a false failure return to computer manufacturers is around 25% of the product's price. The manufacturer absorbs most of that cost, while the retailer is the party able to prevent the returns in the short term, by spending time with customers before the sale and supporting them after it. The retailer bears the cost of that effort but captures only part of its benefit, so without an incentive it exerts too little.

The paper (Ferguson, Guide & Souza, MSOM 2006) models this as a single-period manufacturer–retailer problem with non-contractible retailer effort, and asks which contracts restore the supply chain's optimal effort and who gains from them. It belongs to the literature on supply chain coordination with contracts (Cachon 2003) and on channel rebates with sales effort (Taylor 2002); its object is a target rebate, a payment to the retailer for every false failure return below a target.

Setting

A manufacturer with unit cost $c$ sells to a retailer at wholesale price $w$, who sells at retail price $p$. Avoiding one false failure return is worth

$$M_m = m + \delta_m(w - c) \ \text{to the manufacturer},\qquad R_r = r + \delta_r(p - w)\ \text{to the retailer},$$

where $m$ and $r$ are the parties' return-processing costs and $\delta_m$, $\delta_r$ are the unit sale impacts of avoiding the return (p. 381). Both are assumed positive.

The retailer chooses an effort $\rho \ge 1$ at cost $a\rho^2/2$, $a > 0$. At effort $\rho$ the number of false failures is a nonnegative random variable $X(\rho)$ with mean $\beta/\rho$, where $\beta > 0$ is the expected number at the minimum effort $\rho = 1$. The coordinated supply chain earns

$$\Pi(\rho) = (M_m + R_r)\,\beta\Big(1 - \frac1\rho\Big) - \frac{a\rho^2}{2},$$

maximized at the coordinated effort $\rho^C = [(M_m + R_r)\beta/a]^{1/3}$. Without a contract the retailer earns $\pi_R(\rho) = -a\rho^2/2 + R_r\beta(1 - 1/\rho)$ and chooses the decentralized effort $\rho^D = \max\{(R_r\beta/a)^{1/3}, 1\}$; the manufacturer then earns $\pi_M(\rho^D) = M_m\beta(1 - 1/\rho^D)$.

Under a target rebate contract $(u, T)$ the retailer receives $u$ for every false failure below the target $T$, so the profits become

$$\pi_R(\rho \mid T, u) = u\,E\{[T - X(\rho)]^+\} - \frac{a\rho^2}{2} + R_r\beta\Big(1 - \frac1\rho\Big),\qquad \pi_M(\rho \mid T, u) = M_m\beta\Big(1 - \frac1\rho\Big) - u\,E\{[T - X(\rho)]^+\}.$$

The contract coordinates the supply chain when $\rho^C$ maximizes $\pi_R(\cdot \mid T, u)$ over $\rho \ge 1$. In the uniform case of §3.1, $X(\rho) \sim \mathrm{Uniform}(0, 2\beta/\rho)$, and the contract must satisfy $T < 2\beta/\rho^C$.

Formalization targets

Goal: Proposition 2 (p. 383)

Assume $a, \beta, M_m, R_r > 0$ and $(M_m + R_r)\beta > a$, and let $X(\rho)$ be uniform. For every coordinating contract $(u, T)$ with $u > 0$, $0 < T < 2\beta/\rho^C$,

$$\pi_R(\rho^C \mid T, u) \ge \pi_R(\rho^D) \qquad\text{and}\qquad \Big(\pi_M(\rho^C \mid T, u) \ge \pi_M(\rho^D) \iff \rho^C \ge 2\rho^D\Big).$$

Milestones

The milestones follow the paper's §3–§3.1 and the appendix proof, in attack order: concavity of $\Pi$ and optimality of $\rho^C$ (Eqs. (1)–(2)); $\rho^C > 1$ in the interesting case; optimality of $\rho^D$ (Eqs. (3)–(4)); $\rho^C \ge \rho^D$; Proposition 1 (concavity of the retailer's rebate profit when $\partial^2 F(x\mid\rho)/\partial\rho^2 \le 0$); its uniform instance; the uniform closed form (8); the first-order condition (9); the coordinating target (10) together with the admissibility condition $u > M_m$; the manufacturer's profit $M_m\beta(\rho^C - 2)/\rho^C$ under a coordinating contract (25); the retailer's profit (27); and the retailer's gain in the two cases $\rho^D > 1$ (30) and $\rho^D = 1$ (31).

Significance

The result divides the effect of the contract between the two parties. The retailer is always at least as well off as without a contract; the manufacturer, who pays the rebate, gains exactly when the supply chain's optimal effort is at least twice what the retailer would exert alone. When $\rho^D > 1$ this is equivalent to $M_m \ge 7R_r$ (p. 383), so a target rebate pays for the manufacturer only when its own stake in avoiding a false failure dwarfs the retailer's. The companion result (10) shows that for every rebate $u > M_m$ exactly one admissible coordinating target exists, and none for $u \le M_m$: a coordinating rebate is always larger than the manufacturer's own cost of a return.

The results are proved in the paper by calculus and algebra. None of them has a machine-checked proof that we know of, and nothing on Prove2Me models non-contractible effort or target rebates. The mission produces a checked version of the paper's model with the expectation taken as a genuine integral against the uniform law, a formal notion of coordination as the retailer's optimization, and statements that make explicit which hypotheses each step of the appendix uses. The definitions of effort-dependent profits and coordination are reusable for other effort-inducing contracts in the same paper and in the sales-effort literature.

Difficulty

The algebra of the appendix is short once the first-order condition (9) holds at $\rho^C$. The substance is getting there. Coordination is defined by optimality of $\rho^C$ for the retailer's profit, and that profit involves the expectation $E\{[T - X(\rho)]^+\}$, which is piecewise in $\rho$: it equals $T^2\rho/4\beta$ only while $T \le 2\beta/\rho$, and $T - \beta/\rho$ beyond. Deriving (9) requires showing that $\rho^C$ is an interior maximizer, that the expectation is differentiable there with the closed-form derivative, and that the side condition $T < 2\beta/\rho^C$ keeps $\rho^C$ in the closed-form region. The converse direction of (10), that the formula for $T$ produces a coordinating contract, needs concavity of the piecewise profit on all of $\rho \ge 1$, which is where Proposition 1 enters.

Replacing the expectation by the global formula $T^2\rho/4\beta$ is the tempting shortcut and it changes the problem: for $\rho > 2\beta/T$ the formula exceeds the true expectation, and the retailer's maximizer, hence the meaning of "coordinates", changes with it.

Formalization scope

All parameters are real numbers, bundled in a structure Params; $M_m$ and $R_r$ are Params.Mm and Params.Rr. Effort ranges over $\rho \ge 1$ (Set.Ici 1); statements the paper makes for every positive effort (concavity of $\Pi$, the closed form (8)) are stated on $\rho > 0$. Cube roots are Real.rpow with exponent $1/3$ on positive bases. The uniform law is Lebesgue measure conditioned on $[0, 2\beta/\rho]$, and the expectation is the Bochner integral against it. Coordination is IsMaxOn of the retailer's profit on Set.Ici 1 at $\rho^C$.

Three conventions differ from the printed text, each recorded in the item's formalization note:

1. The interesting case is printed as $(m + r)\beta > a$; the condition equivalent to the stated consequence $\rho^C > 1$, which the proof uses, is $(M_m + R_r)\beta > a$. The formalization uses the latter.
2. The printed evaluation $E_X\{[T - X(\rho)]^+\} = u\int_0^T (T - x)(\rho/2\beta)\,dx$ carries a stray factor $u$; the expectation is $T^2\rho/4\beta$.
3. Proposition 1 is stated for an arbitrary family of probability laws on $[0,\infty)$ whose distribution functions are $C^2$ in $\rho$ with nonpositive second derivative for $x \in [0,T]$; the paper's further assumptions on $F$ (differentiable, strictly increasing in $x$, mean $\beta/\rho$) are not imposed.

The side condition $T < 2\beta/\rho^C$ of §3.1 is a hypothesis of the goal and of the appendix milestones; without it a coordinating contract with $u = M_m$ exists and the "if" direction fails. The unused page assertion $\delta_r < \delta_m < 1$ is not imposed.

The goal is not trivialized by its coordination hypothesis: coordination is the retailer's optimization over the true profit, and milestone (10) shows that coordinating contracts with $T < 2\beta/\rho^C$ exist for every $u > M_m$, so the hypotheses are satisfiable (Example 1 of the paper, p. 384, is an instance). The retailer half of the goal is comparatively short under this definition of coordination; that is a property of the paper's theorem, not of the encoding. The manufacturer half needs (8), (9) and (25).

The development needs only Mathlib: real calculus (derivatives, concavity, Real.rpow) and Lebesgue integration against a conditioned Lebesgue measure. The model definitions (effort-dependent profits, coordination as the retailer's optimization) are reusable for the paper's other effort-inducing contracts. Contributions welcome: proofs of any milestone, and reusable lemmas on expectations of $[T - X]^+$ under uniform laws.

Selected references

• M. Ferguson, V. D. R. Guide Jr., G. C. Souza, Supply Chain Coordination for False Failure Returns, Manufacturing & Service Operations Management 8(4):376–393, 2006. https://doi.org/10.1287/msom.1060.0112
• G. P. Cachon, Supply Chain Coordination with Contracts, in Handbooks in Operations Research and Management Science, Vol. 11: Supply Chain Management, Elsevier, 2003. https://doi.org/10.1016/S0927-0507(03)11006-7
• T. A. Taylor, Supply Chain Coordination Under Channel Rebates with Sales Effort Effects, Management Science 48(8):992–1007, 2002. https://doi.org/10.1287/mnsc.48.8.992.168