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It's just a difference in how the authors decided to write the algorithm. Left-preconditioned GMRES is the same as regular GMRES where $A$ is replaced with $M^{-1}A$ and $b$ is replaced with $M^{-1}b$. (I.e. you solve the preconditioned system $M^{-1}Ax=M^{-1}b$ instead of $Ax=b$.)


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Welcome to SciComp! Do you have any limits of how large your lists of sublength will get? Are you looking for the "optimal" solution, or for one that is "good enough"? The answer to this makes the difference if we are talking about heuristics or hard math, so to say. I'd try to avoid testing all combinations by trying to make the ...


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In general, I think that the answer to your question about computing a single component is negative (unless you allow for Strassen-like methods, which technically are a positive answer since they have lower complexity than LU/Gaussian elimination). The best way to compute determinants in practice is via Gaussian elimination, so that saves you nothing. (Also, ...


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I think either your priority queue is sorted the wrong direction (largest first) or you're using some kind of non-priority queue. The Python code below implements the algorithm you describe and exhibits approximately circular growth. I've also included a switch to turn on a more random growth. #!/usr/bin/env python3 import time from heapq import heappop, ...


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I'd need a TimestepInterval of less than a second - this increases the algorithms computational burden by more than a factor of 10, and is wasteful is food areas with lower RoS. Nonetheless, your existing, simple model is mathematically defensible, easy to explain, and easy to debug. Therefore, I'd recommend that you work on improving the performance of ...


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