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Hamiltonian Monte Carlo and leapfrog correction — Examples

1 · Prerequisites

2 · Summary

The examples verify a globally Lipschitz force and its exact-flow stability bound, an exactly solvable Gaussian flow, a harmonic leapfrog resonance that leaves position unchanged, and a finite-step energy error with its Metropolis acceptance probability. They separate exact target invariance from energy conservation and exploration: the corrected kernel can preserve the target while a resonant trajectory remains trapped at a point.

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

ExampleConstruction: AI-generatedVerification: AI-generatedjudge pass (gpt-6-luna)Open item page →

A globally Lipschitz Hamiltonian force

Example

Take d=1, M=1, and U(q)=1+q2. This gives a proper target, a globally 1-Lipschitz force, and a complete exact Hamiltonian flow with the Gronwall stability estimate from A complete Hamiltonian flow for a C2 potential preserves energy and phase volume.

Facts & Assumptions

Given: AC, U(q)=1+q2, unit mass, and the one-dimensional Euclidean metric.

[F1]

The HMC target is π(q)=ZU−1e−U(q) and its Hamiltonian vector field is F(q,p)=(p,−U′(q)). Hamiltonian Monte Carlo

[F2]

A globally state-Lipschitz ODE vector field has a unique global solution. A globally state-Lipschitz vector field on R×Rn has global solutions

[F3]

Solutions of a state-Lipschitz ODE obey the Gronwall stability estimate. The Grönwall estimate for two solutions of a Lipschitz ODE

[F4]

Under Countable Choice, a complete C2 Hamiltonian flow preserves H and phase volume; a globally Lipschitz gradient gives the stated sufficient completeness and stability bound. A complete Hamiltonian flow for a C2 potential preserves energy and phase volume

[F5]

If a function is continuous on a closed interval and differentiable on its interior, its derivative at some interior point equals the average rate of change. The mean value theorem, as the case g(x)=x of Cauchy's: for f continuous on [a,b] with a<b and differentiable on (a,b) there is c∈(a,b) with f(b)−f(a)=f′(c)(b−a)

[F6]

AC implies Countable Choice, the axiom required for the exact-flow volume result. AC supplies countable selections and prescribed serial paths

[F7]

Under Countable Choice, the function x↦e−∣x∣ is nonnegative, measurable, and Lebesgue integrable. The real-line exponential tail is Lebesgue integrable

[F9]

For nonnegative measurable functions, the nonnegative integral is monotone under pointwise domination. Monotonicity and nonnegative homogeneity of the nonnegative integral

[F10]

On a measure space, for nonnegative measurable f and measurable E, ∫Ef dλ=∫f1E dλ. If f≥c≥0 on E, monotonicity and the simple-function definition give ∫Ef dλ≥cλ(E). Integral over a measurable subset, Monotonicity and nonnegative homogeneity of the nonnegative integral, The integral of a nonnegative simple function

Proof

technique · direct derivative and norm estimates
1.1F5F6F7F8F9F10givenalgebra

For ∣q∣≤1, U(q)≤2, so monotonicity of the exponential gives e−U(q)≥e−2; [F8]–[F10] imply ZU≥∫[−1,1]e−U(q) dλ1(q)≥e−2λ1([−1,1])=2e−2>0. For all q, U(q)≥∣q∣, hence e−U(q)≤e−∣q∣; by [F6] AC supplies the Countable Choice required by [F7], and [F9] gives ZU<∞. Differentiating gives U′(q)=q1+q2,U′′(q)=1(1+q2)3/2∈(0,1]. Applying [F5] to U′, which is continuous on each closed interval and differentiable on its interior, the bound 0<U′′≤1 makes U′ globally 1-Lipschitz.

2.1F1step 1.1algebra

For z=(q,p) and w=(q~,p~), ∥F(z)−F(w)∥22=∣p−p~∣2+∣U′(q)−U′(q~)∣2≤∣p−p~∣2+∣q−q~∣2=∥z−w∥22. Thus the Hamiltonian vector field is globally 1-Lipschitz.

3.1F2F4F6step 2.1

Apply [F2] to obtain the unique global flow. Since U∈C2, [F4] and its Countable Choice hypothesis supplied by [F6] give energy and volume preservation; its sufficient-condition bound has C=max⁡{1,1}=1.

4.1F3step 2.1∎

Applying [F3] with the same constant gives ∥Φt(z)−Φt(w)∥2≤e∣t∣∥z−w∥2 for every pair of initial states and every real t. This is a flow-stability bound, not a discretization-error estimate.

ExampleConstruction: AI-generatedVerification: AI-generatedjudge pass (gpt-6-luna)Open item page →

The exact-flow Hamiltonian Monte Carlo limit

Example

For the standard-normal target U(q)=q2/2 with M=1, exact integration for time π/2, followed by the required momentum flip, proposes an independent standard-normal position and has acceptance probability one. This is the exact-flow limit; a finite leapfrog map generally has a nonzero energy error.

Facts & Assumptions

Given: AC, U(q)=q2/2, M=1, current position q, refreshed momentum p∼N(0,1), and exact integration time τ=π/2.

[F1]

The HMC target is proportional to e−U(q), the kinetic energy is p2/2, and the momentum is refreshed independently from N(0,1). Hamiltonian Monte Carlo

[F2]

The standard normal law has density (2π)−1/2e−x2/2. Standard normal and normal laws

[F3]

Under Countable Choice, U∈C2, and completeness, the exact Hamiltonian flow preserves H and phase volume. A complete Hamiltonian flow for a C2 potential preserves energy and phase volume

[F4]

A globally state-Lipschitz vector field has a unique global solution through each initial state. A globally state-Lipschitz vector field on R×Rn has global solutions

[F5]

AC implies Countable Choice, the axiom required for the exact-flow volume result. AC supplies countable selections and prescribed serial paths

Proof

technique · solve the linear Hamilton equations and apply the exact-flow correction
1.1F1F3F4F5givenalgebra

Here Hamilton's equations are q˙=p and p˙=−q. For z=(q,p) and w=(q~,p~), ∥F(z)−F(w)∥22=∣p−p~∣2+∣q−q~∣2=∥z−w∥22, so the field is globally 1-Lipschitz. The curves q(t)=qcos⁡t+psin⁡t,p(t)=pcos⁡t−qsin⁡t satisfy these equations and the initial condition (q(0),p(0))=(q,p). By [F4], this global solution is the unique exact flow through (q,p); [F3] applies with the Countable Choice hypothesis supplied by [F5].

2.1F1step 1.1algebra

At t=π/2, the exact endpoint is (p,−q); flipping momentum gives z∗=(p,q). Since H(q,p)=(q2+p2)/2, H(z∗)=H(q,p) and the Metropolis acceptance probability is 1.

3.1F1F2step 2.1∎

The returned position is the refreshed p, which is independent of the old q and has law N(0,1) by [F2]. Thus every update returns an independent draw from the target. This exact-flow argument does not apply to a finite leapfrog discretization.

ExampleConstruction: AI-generatedVerification: AI-generatedjudge pass (gpt-6-luna)Open item page →

A Gaussian harmonic leapfrog resonance

Example

For U(q)=q2/2, M=1, ϵ=2, and L=4, the leapfrog trajectory returns exactly to its initial phase point before the momentum flip. The accepted position therefore never moves; starting at q=0 is a trapped state although the standard-normal target assigns positive probability to [1,2].

Facts & Assumptions

Given: AC, the one-dimensional HMC target N(0,1), unit mass, step size ϵ=2, and L=4 leapfrog steps.

[F1]

The one-step map is the composition of a half force kick, full drift, and half force kick. One-step leapfrog map as force and drift shears

[F2]

The HMC proposal flips momentum and accepts according to the Hamiltonian difference. Leapfrog Hamiltonian Monte Carlo

[F3]

Under AC, the standard-normal density is (2π)−1/2e−q2/2. Standard normal and normal laws

[F4]

The HMC proposal is the momentum flip composed with the L-step leapfrog map, and its correction uses the Hamiltonian difference. Leapfrog Hamiltonian Monte Carlo

[F5]

AC implies Countable Choice, the assumption needed for the Lebesgue box measure used below. AC supplies countable selections and prescribed serial paths

[F7]

The probability of a Borel set is the integral of its density; the nonnegative integral is monotone, and a constant c≥0 integrates over E to cλ(E). Integral over a measurable subset, Monotonicity and nonnegative homogeneity of the nonnegative integral, The integral of a nonnegative simple function

Proof

technique · exact matrix multiplication
1.1F1algebra

For U′(q)=q and M=1, the half kick and drift matrices are Kϵ/2=(10−ϵ/21),Dϵ=(1ϵ01). Multiplication in the order from [F1] gives A=Kϵ/2DϵKϵ/2=(1−ϵ2/2ϵ−ϵ(1−ϵ2/4)1−ϵ2/2).

2.1F1F4step 1.1algebra

Substituting ϵ=2 gives A=(02−2/20) and direct multiplication yields A2=−I2, hence A4=I2. Thus after four steps (q4,p4)=(q,p). By [F4] the flipped proposal is (q,−p), whose Hamiltonian equals the initial Hamiltonian because K is even.

3.1F2F3F5F6F7step 2.1algebra∎

By [F2], acceptance is one and every returned position equals the input q. Starting at q=0 therefore gives law δ0 at every iteration. On [1,2], the density [F3] is at least (2π)−1/2e−2>0, so [F6] and [F7] give target mass at least (2π)−1/2e−2λ1([1,2])=(2π)−1/2e−2>0. Thus the chain remains trapped at a point while the target is not a point mass.

ExampleConstruction: AI-generatedVerification: AI-generatedOpen item page →

Finite-step leapfrog energy error

Example

For the one-dimensional standard-normal potential U(q)=q2/2, unit mass, ϵ=1, one leapfrog step, and initial phase point (0,1), the proposed energy rises by 1/8 and the Metropolis acceptance probability is e−1/8.

Facts & Assumptions

Given: AC, H(q,p)=(q2+p2)/2, ϵ=1, L=1, and (q0,p0)=(0,1).

[F1]

A leapfrog step applies a half kick, full drift, then half kick; the proposal flips the final momentum. One-step leapfrog map as force and drift shears The HMC correction uses min⁡{1,e−H(z∗)+H(z)}. Hamiltonian Monte Carlo

[F2]

The algorithm retains the original position if the proposal is rejected. Leapfrog Hamiltonian Monte Carlo

[F3]

The exponential is positive and strictly increasing, and exp⁡(−x)=1/exp⁡(x) for every real x; at x=0, positivity and the reciprocal identity give exp⁡(0)2=1, hence exp⁡(0)=1. The exponential is positive and satisfies exp⁡(−x)=1/exp⁡(x), The exponential function is strictly increasing

Proof

technique · direct substitution
1.1F1givenalgebra

The first half kick gives p1/2=1−12U′(0)=1. The drift gives q1=0+1⋅p1/2=1. The final half kick gives p1=1−12U′(1)=1/2, so the flipped proposal is (q∗,p∗)=(1,−1/2).

2.1givenstep 1.1algebra

The initial energy is H(0,1)=1/2, while the proposed energy is H(1,−1/2)=1/2+1/8=5/8. The discrete step therefore does not conserve H at this point.

3.1F1F2F3step 2.1algebra∎

The acceptance probability is min⁡{1,e−5/8+1/2}=e−1/8<1, since −1/8<0 and [F3] gives e−1/8<e0=1; rejection retains q=0 by [F2]. This finite calculation distinguishes energy error from the exact target-invariance statement proved for the full Metropolized kernel.

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