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A Consensus-Based Algorithm for Non-Convex Multiplayer Games: Conclusionby@oligopoly
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A Consensus-Based Algorithm for Non-Convex Multiplayer Games: Conclusion

by OligopolyJuly 10th, 2024
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Enis Chenchene, Hui Huang, Jinniao Qiu, and H.H.Q. present an algorithm for multi-objective optimization with uniform pareto control. The algorithm is based on the Quantitative Laplace principle and the mean-field limit: non-Lipschastic forces.
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Authors:

(1) Enis Chenchene, Department of Mathematics and Scientific Computing, University of Graz;

(2) Hui Huang, Department of Mathematics and Scientific Computing, University of Graz;

(3) Jinniao Qiu, Department of Mathematics and Statistics, University of Calgary.

Abstract and 1 Introduction

2 Global convergence

2.1 Quantitative Laplace principle

2.2 Global convergence in mean-field law

3 Numerical experiments and 3.1 One-dimensional illustrative example

3.2 Nonlinear oligopoly games with several goods

4 Conclusion, Acknowledgments, Appendix, and References

4 Conclusion

Acknowledgments

The Department of Mathematics and Scientific Computing at the University of Graz, with which E.C. and H.H. are affiliated, is a member of NAWI Graz (https://nawigraz.at/en). E.C. has received funding from the European Union’s Framework Programme for Research and Innovation Horizon 2020 (2014–2020) under the Marie Sk lodowska–Curie Grant Agreement No. 861137. J.Q. is partially supported by the National Science and Engineering Research Council of Canada (NSERC) and by the 2023-2024 PIMS-Europe Fellowship.

Appendix





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This paper is available on arxiv under CC BY 4.0 DEED license.