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315 sats \ 6 replies \ @nullcount 22 May \ on: Amboss Targets Bitcoin Scalability with New Machine Learning Research lightning
I have doubts that advanced pathfinding techniques are even that useful in a highly malleable network like LN.
Pathfinding is huge in static networks like roads (traveling salesman, etc.)
But in the LN, if there is a long path from point A to point B, you can just make a new path (a.k.a. channel) direct to the destination and for pretty cheap and have it operational in less than an hour.
Our benchmark testing is ongoing and the initial results are extremely promising vs off the shelf LND.
In a dynamic graph like Lightning, the need for advanced computation is even greater.
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By "off the shelf LND", do you mean a new LND node that has not yet built a robust probabilistic routing model?
LND uses probabilistic routing to build a local model of payment success probabilities for every attempted path. After many payment attempts, an LND node should optimize its own pathfinding.
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We're testing both zeroed-out mission control and a learned mission control.
Apriori is decidedly difficult to benchmark against!
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I'm suspicious of machine learning path finding in general. Big fan of machine learning. But classical algorithms are already good in these graph algorithms
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The computation and retraining is not intensive at this size of the network graph. As the network graph increases in size, the improvements in payment reliability offered by this type of calculation should increase versus anarchistic, source-based routing.
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