CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration

Gellert Weisz, Andras Gyorgy, Csaba Szepesvari
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6707-6715, 2019.

Abstract

We consider the problem of configuring general-purpose solvers to run efficiently on problem instances drawn from an unknown distribution, a problem of major interest in solver autoconfiguration. Following previous work, we focus on designing algorithms that find a configuration with near-optimal expected capped runtime while doing the least amount of work, with the cap chosen in a configuration-specific way so that most instances are solved. In this paper we present a new algorithm, CapsAndRuns, which finds a near-optimal configuration while using time that scales (in a problem dependent way) with the optimal expected capped runtime, significantly strengthening previous results which could only guarantee a bound that scaled with the potentially much larger optimal expected uncapped runtime. The new algorithm is simpler and more intuitive than the previous methods: first it estimates the optimal runtime cap for each configuration, then it uses a Bernstein race to find a near optimal configuration given the caps. Experiments verify that our method can significantly outperform its competitors.

Cite this Paper

BibTeX
@InProceedings{pmlr-v97-weisz19a, title = {{C}aps{A}nd{R}uns: An Improved Method for Approximately Optimal Algorithm Configuration}, author = {Weisz, Gellert and Gyorgy, Andras and Szepesvari, Csaba}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {6707--6715}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/weisz19a/weisz19a.pdf}, url = {https://proceedings.mlr.press/v97/weisz19a.html}, abstract = {We consider the problem of configuring general-purpose solvers to run efficiently on problem instances drawn from an unknown distribution, a problem of major interest in solver autoconfiguration. Following previous work, we focus on designing algorithms that find a configuration with near-optimal expected capped runtime while doing the least amount of work, with the cap chosen in a configuration-specific way so that most instances are solved. In this paper we present a new algorithm, CapsAndRuns, which finds a near-optimal configuration while using time that scales (in a problem dependent way) with the optimal expected capped runtime, significantly strengthening previous results which could only guarantee a bound that scaled with the potentially much larger optimal expected uncapped runtime. The new algorithm is simpler and more intuitive than the previous methods: first it estimates the optimal runtime cap for each configuration, then it uses a Bernstein race to find a near optimal configuration given the caps. Experiments verify that our method can significantly outperform its competitors.} }
Endnote
%0 Conference Paper %T CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration %A Gellert Weisz %A Andras Gyorgy %A Csaba Szepesvari %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-weisz19a %I PMLR %P 6707--6715 %U https://proceedings.mlr.press/v97/weisz19a.html %V 97 %X We consider the problem of configuring general-purpose solvers to run efficiently on problem instances drawn from an unknown distribution, a problem of major interest in solver autoconfiguration. Following previous work, we focus on designing algorithms that find a configuration with near-optimal expected capped runtime while doing the least amount of work, with the cap chosen in a configuration-specific way so that most instances are solved. In this paper we present a new algorithm, CapsAndRuns, which finds a near-optimal configuration while using time that scales (in a problem dependent way) with the optimal expected capped runtime, significantly strengthening previous results which could only guarantee a bound that scaled with the potentially much larger optimal expected uncapped runtime. The new algorithm is simpler and more intuitive than the previous methods: first it estimates the optimal runtime cap for each configuration, then it uses a Bernstein race to find a near optimal configuration given the caps. Experiments verify that our method can significantly outperform its competitors.
APA
Weisz, G., Gyorgy, A. & Szepesvari, C.. (2019). CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:6707-6715 Available from https://proceedings.mlr.press/v97/weisz19a.html.

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