STOP criterion for cases, where ”smoking gun” feature from Fig. \[SmokingGun\] was not encountered. In practice this still require a lot of computational resources, beyond capabilities of mid-range PC/laptop. That is why curves in Figs. \[SmokingGun\]-\[Convergence3\] are still far from double epsilon, marked with `DBL_EPSILON` dotted line.

![\[Convergence2\] Similar to Fig. \[SmokingGun\], but $K$ has been replaced by number of subsequent best approximations $N$ found so far. This allows for direct comparison of languages of different length. Possible identification is marked by error for next approximation significantly below $e^{-N}$.](figs/Convergence2){width="50.00000%"}

Expected decrease of approximation error for next best one is $1/e \simeq 0.37$ of previous. Therefore, we use $e$-folding name for Criterion 1.

![\[Convergence3\] Similar to Fig. \[Convergence2\], but $N$ has been replaced by number $k_2$ of valid RPN codes tested so far. Power-law is observed, with $\epsilon \propto k_2^{-1}$. Upper axis show estimated by power-law fit $k_3 \simeq k_2^{0.83}$ number of *unique* formulas.](figs/Convergence3){width="50.00000%"}

Remarkable observation is provided by yet another Figure \[Convergence3\]. Instead of $K$ or $N$ we used total number $k_2$ of *valid* RPN codes tested before encountering next approximation. Power law behavior is found, and band of data points (Fig. \[Convergence3\]) can be roughly approximated as proportional to 