TY - GEN
T1 - Fast Hyperparameter Tuning for Ising Machines
AU - Parizy, Matthieu
AU - Kakuko, Norihiro
AU - Togawa, Nozomu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we propose a novel technique to accelerate Ising machines hyperparameter tuning. Firstly, we define Ising machine performance and explain the goal of hyperparameter tuning in regard to this performance definition. Secondly, we compare well-known hyperparameter tuning techniques, namely random sampling and Treestructured Parzen Estimator (TPE) on different combinatorial optimization problems. Thirdly, we propose a new convergence acceleration method for TPE which we call 'FastConvergence'. It aims at limiting the number of required TPE trials to reach best performing hyperparameter values combination. We compare FastConvergence to previously mentioned well-known hyperparameter tuning techniques to show its effectiveness. For experiments, well-known Travel Salesman Problem (TSP) and Quadratic Assignment Problem (QAP) instances are used as input. The Ising machine used is Fujitsu's third generation Digital Annealer (DA). Results show, in most cases, FastConvergence can reach similar results to TPE alone within less than half the number of trials.
AB - In this paper, we propose a novel technique to accelerate Ising machines hyperparameter tuning. Firstly, we define Ising machine performance and explain the goal of hyperparameter tuning in regard to this performance definition. Secondly, we compare well-known hyperparameter tuning techniques, namely random sampling and Treestructured Parzen Estimator (TPE) on different combinatorial optimization problems. Thirdly, we propose a new convergence acceleration method for TPE which we call 'FastConvergence'. It aims at limiting the number of required TPE trials to reach best performing hyperparameter values combination. We compare FastConvergence to previously mentioned well-known hyperparameter tuning techniques to show its effectiveness. For experiments, well-known Travel Salesman Problem (TSP) and Quadratic Assignment Problem (QAP) instances are used as input. The Ising machine used is Fujitsu's third generation Digital Annealer (DA). Results show, in most cases, FastConvergence can reach similar results to TPE alone within less than half the number of trials.
KW - hyperparameter tuning
KW - Ising machine
KW - optimization
UR - http://www.scopus.com/inward/record.url?scp=85149150715&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85149150715&partnerID=8YFLogxK
U2 - 10.1109/ICCE56470.2023.10043382
DO - 10.1109/ICCE56470.2023.10043382
M3 - Conference contribution
AN - SCOPUS:85149150715
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2023 IEEE International Conference on Consumer Electronics, ICCE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Consumer Electronics, ICCE 2023
Y2 - 6 January 2023 through 8 January 2023
ER -