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The online buy trajectory is nondecreasing

Proved
PrimalDualOnline.SkiRental.algX_monotone

by moutei · Sep 16, 2026 · Mathlib 0df444a (Lean v4.33.1)

competitive-analysislinear-programmingonline-algorithmsprimal-dualski-rental

Let B≥1B \ge 1B≥1 be the purchase price of a ski-rental instance and let xjx_jxj​ be the fractional buy variable after jjj updates, run with the rate c∗(B)=(1+1/B)B−1c^{*}(B) = (1 + 1/B)^{B} - 1c∗(B)=(1+1/B)B−1. Then the trajectory is nondecreasing:

a≤b  ⟹  xa≤xbfor all a,b∈N.a \le b \;\Longrightarrow\; x_a \le x_b \qquad \text{for all } a, b \in \mathbb{N}.a≤b⟹xa​≤xb​for all a,b∈N.

This is the formal content of the online requirement the source imposes on the model. Chapter 3 states it twice: "The online requirement is that previous decisions cannot be regretted. That is, if we already rented skies yesterday, we cannot change this decision today. This requirement is captured in the primal linear program by the restriction that the primal variables cannot be decreased" (p. 17–18), and again when the fractional algorithm is introduced: "we require that they cannot be decreased during the execution of the algorithm" (p. 18).

Without it, the cost accounting would not describe an online algorithm at all: a procedure permitted to lower xxx could revisit and undo a purchase commitment it had already made, which is precisely what the model forbids.

Source correspondence.

  • What the thesis states: that the fractional primal variables may not decrease during execution — a requirement on the model, stated in prose, not a numbered lemma.
  • What this Lean theorem states: that the particular trajectory generated by the mission's algX at rate c∗(B)c^{*}(B)c∗(B) is a monotone function of the day index.
  • Strengthenings introduced by the formalization: this turns a model requirement into a proved property of the specific recursion. The source imposes non-decrease as a constraint on admissible algorithms; here it is derived for the algorithm actually defined. Nothing stronger is claimed — not strict increase, no bound on the values, no limit, and nothing about any other rate parameter.
Preamble
import Definitions.Def_PrimalDualOnline_SkiRentalLP
import Definitions.Def_PrimalDualOnline_SkiRentalAlg
import Mathlib.Tactic

open PrimalDualOnline.SkiRental
Formal statement
theorem PrimalDualOnline.SkiRental.algX_monotone
    (B : ℕ) (hB : 0 < B) :
    Monotone (algX B (cOpt B)) := by sorry
Source
Niv Buchbinder, "Designing Competitive Online Algorithms via a Primal-Dual Approach", PhD thesis, Tel Aviv University, 2008, https://www.tau.ac.il/~nivb/download/phd-thsis.pdf, Chapter 3, pp. 17-18 (PDF pp. 33-34): the online requirement stated in prose ("The online requirement is that previous decisions cannot be regretted... captured in the primal linear program by the restriction that the primal variables cannot be decreased"), and restated for the fractional algorithm on p. 18 ("we require that they cannot be decreased during the execution of the algorithm").
Read-back

What the Lean code literally says, in plain math · claude-opus-5

Binders. The statement takes two arguments: a natural number BBB (explicit), and a hypothesis hB:0<Bh_B : 0 < BhB​:0<B. There are no other variables, no implicit arguments, and no typeclass assumptions. In particular the real parameter ccc of the underlying sequence is not quantified: it is fixed to the single value copt(B)c_{\mathrm{opt}}(B)copt​(B).

The objects involved. For a natural number BBB and a real number ccc, the sequence xB,c:N→Rx^{B,c} : \mathbb{N} \to \mathbb{R}xB,c:N→R is defined by recursion on its index:

x0B,c=0,xj+1B,c={xjB,c⋅(1+1B)+1c⋅B,if xjB,c<1,xjB,c,if xjB,c≥1,x^{B,c}_0 = 0, \qquad x^{B,c}_{j+1} = \begin{cases} x^{B,c}_j \cdot \left(1 + \dfrac{1}{B}\right) + \dfrac{1}{c \cdot B}, & \text{if } x^{B,c}_j < 1, \\[2mm] x^{B,c}_j, & \text{if } x^{B,c}_j \ge 1, \end{cases}x0B,c​=0,xj+1B,c​=⎩⎨⎧​xjB,c​⋅(1+B1​)+c⋅B1​,xjB,c​,​if xjB,c​<1,if xjB,c​≥1,​

where BBB is coerced from N\mathbb{N}N into R\mathbb{R}R in both occurrences, and the branch test is the strict inequality xjB,c<1x^{B,c}_j < 1xjB,c​<1 (so the value exactly 111 takes the second, stationary branch). The real constant is

copt(B)=(1+1B)B−1,c_{\mathrm{opt}}(B) = \left(1 + \frac{1}{B}\right)^{B} - 1,copt​(B)=(1+B1​)B−1,

with BBB coerced to R\mathbb{R}R inside the base and used as a natural-number exponent. Both are plain definitions with no side conditions; all divisions are total real division, so a zero denominator yields 000 rather than being undefined.

What is asserted. For every natural number BBB with 0<B0 < B0<B, the function j↦xjB, copt(B)j \mapsto x^{B,\,c_{\mathrm{opt}}(B)}_jj↦xjB,copt​(B)​ is monotone in Mathlib's sense. For a function on N\mathbb{N}N ordered by the usual ≤\le≤, this unfolds to the universally quantified, non-strict implication

∀ a,b∈N,a≤b  ⟶  xaB, copt(B)  ≤  xbB, copt(B).\forall\, a, b \in \mathbb{N}, \quad a \le b \;\longrightarrow\; x^{B,\,c_{\mathrm{opt}}(B)}_a \;\le\; x^{B,\,c_{\mathrm{opt}}(B)}_b .∀a,b∈N,a≤b⟶xaB,copt​(B)​≤xbB,copt​(B)​.

Both index quantifiers are universal, the antecedent is the weak order on naturals, and the conclusion is the weak order on reals. The case a=ba = ba=b is included and holds trivially; the quantifiers range over all natural indices.

What is not asserted. The statement does not claim strict increase, nor strict increase at any particular step; consecutive values may be equal, and the second branch of the recursion makes the sequence stationary once a value ≥1\ge 1≥1 is reached. It states no upper or lower bound on the values — nothing says they lie in [0,1][0,1][0,1], nothing says they are nonnegative, nothing says they ever reach or stay below 111. It states nothing about a limit, convergence, supremum, or eventual behaviour, and nothing about the number of steps needed for any event. There is no finite horizon, index range, or truncation anywhere: the function is defined on all of N\mathbb{N}N and the claim is about all indices. Nothing is claimed about any other value of the parameter ccc, and nothing about copt(B)c_{\mathrm{opt}}(B)copt​(B) itself — no sign, no bound, no optimality or extremal property, no relation to any cost, ratio, or algorithm. There is no mention of any cost function, competitive ratio, adversary, primal or dual objective, or feasibility condition.

Degenerate cases. The hypothesis 0<B0 < B0<B excludes exactly B=0B = 0B=0. Given total division, B=0B = 0B=0 is not a meaningless instance but a concrete one: 1/0=01/0 = 01/0=0, hence copt(0)=(1+0)0−1=0c_{\mathrm{opt}}(0) = (1+0)^0 - 1 = 0copt​(0)=(1+0)0−1=0, hence the increment is 1/0=01/0 = 01/0=0, so the recursion for B=0B = 0B=0 would read xj+1=xjx_{j+1} = x_jxj+1​=xj​ on the branch xj<1x_j < 1xj​<1 — the constant zero sequence. The hypothesis removes that instance. At B=1B = 1B=1 one has copt(1)=1c_{\mathrm{opt}}(1) = 1copt​(1)=1 and the recursion is xj+1=2xj+1x_{j+1} = 2x_j + 1xj+1​=2xj​+1 while xj<1x_j < 1xj​<1, starting from x0=0x_0 = 0x0​=0; so x1=1x_1 = 1x1​=1, and from then on the guard fails and the sequence is constantly 111 — a case where the asserted inequality holds with equality for all a,b≥1a, b \ge 1a,b≥1.

Human review
  • Endorsed by Shuze Chen · Sep 16, 2026

  • Endorsed by moutei · Sep 16, 2026

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

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