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Timeline for Precision in Graph methods

Current License: CC BY-SA 4.0

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Dec 18, 2021 at 21:51 answer added user46831 timeline score: 2
Dec 18, 2021 at 14:20 comment added user46831 Szabolcs, yes, I understand the tradeoff between speed and precision (and memory). That balance is managed so nicely in tools like NDSolve and Minimize. I'm trying to solve a Brownian drift control problem. Proving optimality of the solution requires high precision -- often WorkingPrecision greater than 100. To construct the functions proving optimality requires Column Generation: Maximize (an LP) yields dual variables that translate to edge weights for ShortestPath that identifies new columns to add to the optimization. NDSolve was brilliant in getting the solution. Now I need the proof.
Dec 17, 2021 at 16:31 comment added Szabolcs I am just curious: what is the application for which you need shortest paths with higher than machine precision?
Dec 17, 2021 at 16:30 comment added Szabolcs Some graph metrics can use exact numbers and arbitrary precision numbers, some cannot. As you yourself show, this is not the case for shortest paths. While it could be implemented, it does not seem to be (note that if it were, it would come with a performance impact; in order to retain the current performance, they'd possibly need to keep two separate implementations, a fast machine precision one and a slow exact one).
Dec 17, 2021 at 12:00 history tweeted twitter.com/StackMma/status/1471812447468195843
Dec 17, 2021 at 3:55 comment added MarcoB I am no expert in Mathematica's graph functionality but, if I had to guess, I'd say that this might be a limitation of whatever external library is used under the hood to implement this functionality.
Dec 17, 2021 at 3:33 history edited MarcoB CC BY-SA 4.0
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Dec 16, 2021 at 17:49 history edited J. M.'s missing motivation
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Dec 16, 2021 at 17:36 history asked user46831 CC BY-SA 4.0