Doostmohammadian, M.; Ghods, A.A.; Aghasi, A.; Gabidullina, Z.R.; Rabiee, H.R.
Using Non-Lipschitz Signum-Based Functions for Distributed Optimization and Machine Learning: Trade-Off Between Convergence Rate and Optimality Gap. Math. Comput. Appl. 2025, 30, 108.
https://doi.org/10.3390/mca30050108
AMA Style
Doostmohammadian M, Ghods AA, Aghasi A, Gabidullina ZR, Rabiee HR.
Using Non-Lipschitz Signum-Based Functions for Distributed Optimization and Machine Learning: Trade-Off Between Convergence Rate and Optimality Gap. Mathematical and Computational Applications. 2025; 30(5):108.
https://doi.org/10.3390/mca30050108
Chicago/Turabian Style
Doostmohammadian, Mohammadreza, Amir Ahmad Ghods, Alireza Aghasi, Zulfiya R. Gabidullina, and Hamid R. Rabiee.
2025. "Using Non-Lipschitz Signum-Based Functions for Distributed Optimization and Machine Learning: Trade-Off Between Convergence Rate and Optimality Gap" Mathematical and Computational Applications 30, no. 5: 108.
https://doi.org/10.3390/mca30050108
APA Style
Doostmohammadian, M., Ghods, A. A., Aghasi, A., Gabidullina, Z. R., & Rabiee, H. R.
(2025). Using Non-Lipschitz Signum-Based Functions for Distributed Optimization and Machine Learning: Trade-Off Between Convergence Rate and Optimality Gap. Mathematical and Computational Applications, 30(5), 108.
https://doi.org/10.3390/mca30050108
