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Resource Allocation for Multi-tasking Optimization: Explanation of an Empirical Formula

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Uncertainty, Constraints, and Decision Making

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 484))

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Abstract

For multi-tasking optimization problems, it has been empirically shown that the most effective resource allocation is attained when we assume that the gain of each task logarithmically depends on the computation time allocated to this task. In this paper, we provide a theoretical explanation for this empirical fact.

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References

  1. J. Aczél, J. Dhombres, Functional Equations in Several Variables (Cambridge University Press, Cambridge, 2008)

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  2. T. Wei, J. Zhong, Towards generalized resource allocation on evolutionary multitasking for multi-objective optimization, in IEEE Computational Intelligence Magazine (2021), pp. 20–36

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Acknowledgements

This work was supported in part by the National Science Foundation grants:

\(\bullet \) 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science), and

\(\bullet \) HRD-1834620 and HRD-2034030 (CAHSI Includes).

It was also supported by the AT&T Fellowship in Information Technology, and by the program of the development of the Scientific-Educational Mathematical Center of Volga Federal District No. 075-02-2020-1478.

The authors are thankful to the participants of the 2022 UTEP/NMSU Workshop on Mathematics, Computer Science, and Computational Science (El Paso, Texas, November 5, 2022) for valuable discussions.

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Authors and Affiliations

  1. Department of Computer Science, University of Texas at El Paso, 500 W. University El Paso, El Paso, TX, 79968, USA

    Alan Gamez, Antonio Aguirre, Christian Cordova, Alberto Miranda & Vladik Kreinovich

Authors
  1. Alan Gamez
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  2. Antonio Aguirre
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  3. Christian Cordova
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  4. Alberto Miranda
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  5. Vladik Kreinovich
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Corresponding author

Correspondence to Vladik Kreinovich .

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Editors and Affiliations

  1. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Martine Ceberio

  2. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

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Gamez, A., Aguirre, A., Cordova, C., Miranda, A., Kreinovich, V. (2023). Resource Allocation for Multi-tasking Optimization: Explanation of an Empirical Formula. In: Ceberio, M., Kreinovich, V. (eds) Uncertainty, Constraints, and Decision Making. Studies in Systems, Decision and Control, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-36394-8_13

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