Annealing by Increasing Resampling in the Unified View of Simulated Annealing

Yasunobu Imamura, Naoya Higuchi, Takeshi Shinohara, Kouichi Hirata, Tetsuji Kuboyama

Abstract

Annealing by Increasing Resampling (AIR) is a stochastic hill-climbing optimization by resampling with increasing size for evaluating an objective function. In this paper, we introduce a unified view of the conventional Simulated Annealing (SA) and AIR. In this view, we generalize both SA and AIR to a stochastic hill-climbing for objective functions with stochastic fluctuations, i.e., logit and probit, respectively. Since the logit function is approximated by the probit function, we show that AIR is regarded as an approximation of SA. The experimental results on sparse pivot selection and annealing-based clustering also support that AIR is an approximation of SA. Moreover, when an objective function requires a large number of samples, AIR is much faster than SA without sacrificing the quality of the results.

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Paper Citation


in Harvard Style

Imamura Y., Higuchi N., Shinohara T., Hirata K. and Kuboyama T. (2019). Annealing by Increasing Resampling in the Unified View of Simulated Annealing.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 173-180. DOI: 10.5220/0007380701730180


in Bibtex Style

@conference{icpram19,
author={Yasunobu Imamura and Naoya Higuchi and Takeshi Shinohara and Kouichi Hirata and Tetsuji Kuboyama},
title={Annealing by Increasing Resampling in the Unified View of Simulated Annealing},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={173-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007380701730180},
isbn={978-989-758-351-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Annealing by Increasing Resampling in the Unified View of Simulated Annealing
SN - 978-989-758-351-3
AU - Imamura Y.
AU - Higuchi N.
AU - Shinohara T.
AU - Hirata K.
AU - Kuboyama T.
PY - 2019
SP - 173
EP - 180
DO - 10.5220/0007380701730180