In this section
@article{Schindler:2020:2632-2153/ab94c5,
author = {Schindler, F and Jermyn, AS},
doi = {2632-2153/ab94c5},
journal = {Machine Learning: Science and Technology},
pages = {1--13},
title = {Algorithms for tensor network contraction ordering},
url = {http://dx.doi.org/10.1088/2632-2153/ab94c5},
volume = {1},
year = {2020}
}
TY - JOUR
AB - Contracting tensor networks is often computationally demanding. Well-designed contraction sequences can dramatically reduce the contraction cost. We explore the performance of simulated annealing and genetic algorithms, two common discrete optimization techniques, to this ordering problem. We benchmark their performance as well as that of the commonly-used greedy search on physically relevant tensor networks. Where computationally feasible, we also compare them with the optimal contraction sequence obtained by an exhaustive search. Furthermore, we present a systematic comparison with state-of-the-art tree decomposition and graph partitioning algorithms in the context of random regular graph tensor networks. We find that the algorithms we consider consistently outperform a greedy search given equal computational resources, with an advantage that scales with tensor network size. We compare the obtained contraction sequences and identify signs of highly non-local optimization, with the more sophisticated algorithms sacrificing run-time early in the contraction for better overall performance.Contracting tensor networks is often computationally demanding. Well-designed contraction sequences can dramatically reduce the contraction cost. We explore the performance of simulated annealing and genetic algorithms, two common discrete optimization techniques, to this ordering problem. We benchmark their performance as well as that of the commonly-used greedy search on physically relevant tensor networks. Where computationally feasible, we also compare them with the optimal contraction sequence obtained by an exhaustive search. Furthermore, we present a systematic comparison with state-of-the-art tree decomposition and graph partitioning algorithms in the context of random regular graph tensor networks. We find that the algorithms we consider consistently outperform a greedy search given equal computational resources, with an advantage that scales with tensor network size. We co
AU - Schindler,F
AU - Jermyn,AS
DO - 2632-2153/ab94c5
EP - 13
PY - 2020///
SN - 2632-2153
SP - 1
TI - Algorithms for tensor network contraction ordering
T2 - Machine Learning: Science and Technology
UR - http://dx.doi.org/10.1088/2632-2153/ab94c5
UR - http://hdl.handle.net/10044/1/105854
VL - 1
ER -
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