Path-Extrema Upper Bounds on Mean Entropy Production

Fluctuation relations imply the second-law inequality $\langle\Sigma_T\rangle\ge0$, but path extrema can also constrain how large the mean entropy production can be. For steady-state processes with entropy-production martingale $M_t=e^{-\Sigma_t}$, we show that knowing only the positive running maximum of $\Sigma_t$ gives no improvement over the trivial endpoint bound: rare negative entropy-production excursions can still carry the exponential weight required by the fluctuation relation. Using the running extrema $L_T=\inf M_t$ and $H_T=\sup M_t$, we derive a path-extrema upper envelope $\mathcal{U}_{\rm ext}$. The relaxed envelope problem ranks realized intervals by the entropy gain per martingale cost, $\ln(H_T/L_T)/(H_T-L_T)$, giving a continuous knapsack problem. The actual mean satisfies the exact identity $\langle\Sigma_T\rangle=\mathcal{U}_{\rm ext}-\mathcal{A}-\mathcal{C}$, where $\mathcal{A}$ is an allocation gap across realized envelopes and $\mathcal{C}$ is a curvature gap within each envelope. Thus path extrema set the upper envelope, while the two gaps quantify how actual dynamics allocate terminal outcomes across envelope classes and place terminal values within each realized envelope. This turns path-extrema information into a quantitative upper-bound theory for entropy production, complementary to the usual lower-bound role of fluctuation relations.