This work proposes solver-level differentiation, which differentiates the executed solver itself through the identical executed forward to machine precision at every solver depth; in a controlled same-codebase, same-GPU comparison it is 33x faster and uses 71x less memory than unrolled automatic differentiation.
This work proposes a variant of alternating direction method of multiplier (ADMM), called BC-ADMM, which relaxation leads to a variant of ADMM with convergence speed guarantees under appropriate parameter choices, and provides a practical algorithm under much milder assumptions on the parameter choices, with convergenc...
Zherong Pan, Kui Wu· The international journal of...· 0 citations
The intent is not to survey recent work, but to equip attendees with the concepts, implementation details, and the critical perspective needed to start working with LBM solvers.
Wei Li, Chaoyang Lyu, Mengyun Liu et al.· Proceedings of the Special I...· 1 citation· ⚡1
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