Alphabeta Math
ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedprecheck passjudge pass (gpt-6-luna)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Euler–Maruyama for a constant-coefficient additive-noise equation

Facts & Assumptions

Given: AC, T=1, h=1/n for an integer n≥1, and X0=0. Work on a complete filtered probability space whose filtration is right-continuous and contains every null subset in F0. Let W be a scalar standard Brownian motion adapted to this filtration, with Wt−Ws independent of Fs for every 0≤s<t≤1. Take b(t,x)=1 and σ(t,x)=1.

[F1]

The Euler–Maruyama step is Yk+1=Yk+b(tk,Yk)h+σ(tk,Yk)ΔWk. Euler–Maruyama recursion

[F2]

On [tk,tk+1], the interpolation is Yt=Yk+b(tk,Yk)(t−tk)+σ(tk,Yk)(Wt−Wtk). Euler–Maruyama step process and continuous interpolation

[F3]

The uniform-moment theorem bounds sup⁡t≤TE∣Yt∣p whenever E∣X0∣p<∞. Euler–Maruyama uniform moments and interpolation increments

[F4]

The Itô integral of the constant integrand 1 equals the Brownian increment. Ito integral for square-integrable predictable processes

[F5]

A strong solution is an adapted continuous process satisfying the finite-coordinate Itô equation indistinguishably. Itô SDE data and strong solution

[F6]

The finite-energy Itô-integral process has a continuous martingale version, and any two such continuous versions agree at all times on a measurable probability-one event. The Ito integral process has a continuous martingale version

[A1]

AC is inherited from the Brownian-motion and Itô-integral interfaces; the explicit calculation below makes no choice. The Axiom of Choice

Example

Consider the scalar additive-noise equation dXt=dt+dWt on [0,1]. The coefficients are constant, and for each n the uniform mesh has n steps of length h=1/n.

Verification

1.1givenalgebra

The functions b=1 and σ=1 are jointly Borel, globally Lipschitz in state with constant 0, and satisfy the stated linear-growth bound with a finite K. Their time increments vanish, so the required half-Hölder time modulus holds. The deterministic initial value X0=0 is F0-measurable, independent of future Brownian increments, and has every finite moment.

1.2F1F2F4F5F6algebra

From [F1], induction gives Yk=kh+Wkh. Substituting the coefficients into [F2] gives Yt=kh+(t−kh)+Wkh+Wt−Wkh=t+Wt on every interval. By [F4], at every fixed time the candidate path Xt=t+Wt satisfies the integral equation. The continuous-version theorem [F6] and equality at all rational times identify these processes indistinguishably; hence [F5] makes this adapted continuous candidate a strong solution. Any strong solution with the same data equals t+Wt directly from the constant-coefficient equation, so this is the exact path.

2.1A1F3step 1.2algebra∎

The initial value X0=0 has every finite moment. Applying [F3] for each finite p≥2 therefore proves finite moments of the grid, step, and interpolated processes uniformly in n and t≤1. The explicit identity Yt=t+Wt from step 1.2 also identifies the exact path. AC in [A1] is inherited from the cited interfaces; no choice enters these computations.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

48 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources