Minmax Propagation. Ravanbakhsh, S., Srinivasa, C., & Frey, B. In 2017. pdf coming soon!abstract bibtex "We study the application of min-max propagation, a variation of belief propagation, for approximate min-max inference in factor graphs. We show that for ``any'' high-order functions that can be minimized in O(m), the min-max message update can be obtained using an efficient O(K (m + log(K)) procedure, where K is the number of variables.We demonstrate how this generic procedure, in combination with efficient updates for a family of high-order constraints, enables the application of min-max propagation to efficiently approximate the NP-hard problem of makespan minimization, which seeks to distribute a set of tasks on machines, such that the worst case load is minimized."
@InProceedings{chris_minmax_prop,
author={Ravanbakhsh, Siamak and Srinivasa, Christopher and Frey, Brendan},
title={Minmax Propagation},
journal = {Proceedings of the Neural Information Processing Systems (NIPS)},
shortbooktitle={NIPS},
location= { Long Beach , CA},
year = {2017},
order={2},
year={2017},
Bibbase_Note={<font> pdf coming soon!</font>},
abstract={"We study the application of min-max propagation, a variation of belief propagation,
for approximate min-max inference in factor graphs.
We show that for ``any'' high-order functions that can be minimized in O(m), the min-max message update can be obtained using an efficient O(K (m + log(K)) procedure,
where K is the number of variables.We demonstrate how this generic procedure, in combination with efficient updates for a family of high-order constraints,
enables the application of min-max propagation to efficiently approximate the NP-hard problem of makespan minimization,
which seeks to distribute a set of tasks on machines, such that the worst case load is minimized."},
}
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