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Reproduction logbook (paper-69IOkVkTQX)
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Executive summary

Paper setting

The paper studies convex minimization on a compact convex domain with a Bregman regularizer. Its Lévy mirror flow is

[ dY(t)=-\nabla f(X(t)),dt+dL(t),\qquad X(t)=\operatorname{mirror}(\eta(t)Y(t)), ]

where the centered Lévy process has finite p-th moments for p in (1,2] and can have jumps with infinite variance when p<2. The discrete stochastic dual averaging update is Y[t+1]=Y[t]-(∇f(X[t])+noise[t]) followed by X[t+1]=mirror(η[t+1]Y[t+1]).

The reproduction uses the paper’s bounded Euclidean instance X=[-1,1], h(x)=x²/2, and mirror(z)=clip(ηz,-1,1). Symmetric Pareto compound-Poisson jumps have tail index alpha=p+0.12, so their p-th moment is finite while the p<2 cases remain heavy-tailed. Brownian increments provide the tame component where stated. Huber objectives are convex and 1/δ-smooth on each tested branch; quadratic objectives are used for strong-convexity controls.

Independent evidence

claim seeds/trials sweep decisive result
1 8 seeds p={1.25,1.5,1.75,2}, T={256,…,4096} 4/4 measured gaps below the paper’s explicit R0(T) bound; gap varies across both sweeps
2 8 seeds per cell 5 eta values, three heavy p values plus tame p=1.5 every measured stationary MSE is below the paper radius; heavy and tame treatments have distinct eta slopes
3 8 seeds per cell p={1.5,1.75}, five δ values 40/40 first-passage cells uncensored and below the explicit Theorem-3 bound
4 8 seeds p={1.25,1.5,1.75,2}, T={512,…,8192} SDA and LMF gaps vary with T and pass their respective paper rate bounds in every displayed cell
5 20 seeds per cell p={1.5,1.75}, four ε values measured iteration counts increase as ε tightens; reverse-drift control reaches 0/20 seeds
6 12 paths per branch Huber thresholds a={1,1.5,2} correction-on inequality passes 12/12 in every branch; correction-off control passes 0/12

The code computes means and standard errors across independent seeds. No paper figure, printed result, or peer logbook is parsed as a result source.