In some cases I do this manually, in a crude manner guided by intuition. I don't know enough to program it. I don't believe we know a simple metric such as LCM that would be sufficient on its own to provide a useful ordering.
This is based on simple principles:
1. The probability of success when checking a single candidate
2. The change in the probability of success as increase the numbers in the chains
3. If we check up to a fixed value, the number of checks in a given pattern/batch
But upon further examination, it turns out that these simple factors depend intricately on many others. And that's sad.
However, approaches have already been developed for assessing the viability of certain batches, at least roughly.
These approaches are unlikely to be programmed into code right now.
The question arises: is it worth spending several weeks on a semi-manual analysis, or is it better to launch a brute force calculation immediately?
For your purposes, the answer to this question depends, among other things, on the probability of improving the existing lower bound.
And the answer to this question depends on how this lower bound was obtained.
Ranking batches makes sense if:
a) the calculation will take a long time (more than 2-3 months, for example)
b) the method for finding the current boundary suggests an improvement of an order of magnitude or more.
Then, yes, you can spend 1-2 weeks evaluating different batch types, programming batch calculation runs in a different order than how pcoul generates them.
-- добавлено через 15 минут --And the answer to this question depends on how this lower bound was obtained.
F.e. I don't know how anyone got the upper bound of D(48.10), which you improved by an order of magnitude.
But after the fact, looking at the batch in which the improvement was found, I can say that yes, the improvement was very likely.