Published or accepted

1. H. Huang, J. Qiu, K. Riedl, Consensus-based optimization for saddle point problems, SIAM Journal on Control and Optimization, 2023, to appear.

2. H. Huang, J. Qiu, K. Riedl, On the Global Convergence of Particle Swarm Optimization Methods, Applied Mathematics & Optimization, 30(2023) 88:30.

3. H. Huang, H. Park, Optimal consensus control models on the sphere, Results in Control and Optimization, 10(2023) 100203.

4. B. Bonnet, C, Cipriani, M. Fornasier, H. Huang, A Measure Theoretical Approach to the Mean-field Maximum Principle for Training NeurODEs, Nonlinear Analysis, 227(2023) 113161.

5. M. Fornasier, H. Huang, L. Pareschi and P. Sünnen, Anisotropic Diffusion in Consensus-based Optimization on the Sphere, SIAM Journal on Optimization, 32(2012) 1984-2012. 

6. R. C. Fetecau, H. Huang, D. Messenger and W. Sun, Zero-diffusion limit for aggregation equations over bounded domains, Discrete and Continuous Dynamical Systems - Series A, 42(2022) 4905-4936. (PDF )

7. C. Cipriani, H.Huang, J. Qiu, Zero-inertia limit: from particle swarm optimization to consensus-based optimization, SIAM Journal on Mathematical Analysis, 54(2022) 3091-3121. (PDF )

8. H.Huang, J. Qiu, On the mean-field limit for the consensus-based optimization, Mathematical Methods in the Applied Sciences, 1(2022) 1-18. (PDF )

9. H. Huang, Quantitative estimate of the overdamped limit for the Vlasov-Fokker-Planck systems, Partial Differential Equations in Applied Mathematics, 4(2021) 100186. (PDF) 

10. M. Fornasier, H. Huang, L. Pareschi and P. Sünnen, Consensus-based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning, Journal of Machine Learning Research, 22 (2021) 1-55. (PDF) 

11. H. Huang, A note on the mean-field limit for the particle swarm optimization, Applied Mathematics Letters, 117(2021), 107133. (PDF)

12.  H. Huang and J. Qiu, The microscopic derivation and well-posedness of the stochastic Keller-Segel equation, Journal of Nonlinear Science, 31(2021), 1-31. (PDF)

13. M. Fornasier, H. Huang, L. Pareschi and P. Sünnen, Consensus-Based Optimization on Hypersurfaces: Well-Posedness and Mean-Field Limit, Mathematical Models and Methods in Applied Sciences, 30(2020), 2725-2751. (PDF)

14. H. Huang, J.-G. Liu and P. Pickl, On the mean-field limit for the Vlasov-Poisson-Fokker-Planck system, Journal of Statistical Physics, 181(2020), 1915–1965. (PDF) 

15. H. Huang, J.-G. Liu and J. Lu, Learning interacting particle systems: diffusion parameter estimation for aggregation equations, Mathematical Models and Methods in Applied Sciences, 29 (2019), 1-29. (PDF)

16. R. C. Fetecau, H. Huang and W. Sun, Propagation of chaos for the Keller-Segel equation over bounded domains, Journal of Differential Equations, 266 (2019), 2142-2174. (PDF)

17. H. Huang and J.-G. Liu, Discrete-in-time random particle blob method for the Keller-Segel equation and convergence analysis, Communications in Mathematical Sciences, 15 (2017), 1821-1842. (PDF)

18. H. Huang and J.-G. Liu, Error estimate of a random particle blob method for the Keller-Segel equation, Mathematics of Computation, 86 (2017), 2719-2744. (PDF)

19. H. Huang and J.-G. Liu, Error estimates of the aggregation-diffusion splitting algorithms for the Keller-Segel equations, Discrete and Continuous Dynamical Systems - Series B, 21 (2016) 3463-3478. (PDF)

20. H. Huang and J.-G. Liu, A note on Monge-Ampere Keller-Segel equation, Applied Mathematics Letters, 61 (2016) 26-34. (PDF)

21. H. Huang and J.-G. Liu, Well-posedness for the Keller-Segel equation with fractional Laplacian and the theory of propagation of chaos, Kinetic and Related Models, 9 (2016) 715-748. (PDF)

Preprints

1. S. Grassi, H. Huang, L. Pareschi, J. Qiu, Mean-field particle swarm optimization, submitted. arXiv:2108.00393