Скачать или смотреть Revolutionizing Optimization: Unveiling Gradient-Free Methods and Their Impact

This episode explores the revolutionary advancements in Optimization Theory, focusing on gradient-free methods and their increasing impact across various scientific and engineering domains.


Introduction to gradient-free optimization and its departure from traditional gradient-based methods.
Evolutionary algorithms and their adaptability to complex, non-differentiable problems.
Applications in hyperparameter optimization, structural design, and reinforcement learning.
Challenges and future directions, including improving efficiency, scalability, and theoretical guarantees.


Recent research, such as Abdennour Boulesnane's exploration of Evolutionary Dynamic Optimization and Machine Learning (http://arxiv.org/abs/2310.08748v3) and Li Yang and Abdallah Shami's study on Hyperparameter Optimization of Machine Learning Algorithms (http://arxiv.org/abs/2007.15745v3), showcases the versatility of gradient-free methods in tackling complex, non-differentiable problems.


Gradient-free methods find practical applications in optimizing machine learning models, designing robust engineering structures, and even optimizing radiation therapy plans in healthcare, demonstrating their versatility beyond traditional optimization domains.


Future research will likely focus on improving the efficiency and scalability of these methods, exploring hybrid approaches that combine gradient-based and gradient-free techniques, and extending their application to new and challenging problem domains.


References
Abdennour Boulesnane (2023). Evolutionary Dynamic Optimization and Machine Learning. Available: http://arxiv.org/abs/2310.08748v3 DOI: 10.xxxx/xxxx
Li Yang, Abdallah Shami (2020). On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice. Available: http://arxiv.org/abs/2007.15745v3 DOI: 10.xxxx/xxxx
Mehran Ebrahimi, Hyunmin Cheong, Pradeep Kumar Jayaramanet al. (2024). Optimal design of frame structures with mixed categorical and continuous design variables using the Gumbel-Softmax method. Available: http://arxiv.org/abs/2501.00258v1 DOI: 10.xxxx/xxxx
Hassan Rafique, Mingrui Liu, Qihang Linet al. (2018). Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning. Available: http://arxiv.org/abs/1810.02060v4 DOI: 10.xxxx/xxxx
Sébastien Bubeck (2014). Convex Optimization: Algorithms and Complexity. Available: http://arxiv.org/abs/1405.4980v2 DOI: 10.xxxx/xxxx
Valentin Leplat, Yurii Nesterov, Nicolas Gilliset al. (2021). Conic-Optimization Based Algorithms for Nonnegative Matrix Factorization. Available: http://arxiv.org/abs/2105.13646v3 DOI: 10.xxxx/xxxx
Tengyu Xu, Zhe Wang, Yingbin Lianget al. (2020). Gradient Free Minimax Optimization: Variance Reduction and Faster Convergence. Available: http://arxiv.org/abs/2006.09361v3 DOI: 10.xxxx/xxxx
Haipeng Luo, Patrick Haffner, Jean-Francois Paiement (2014). Accelerated Parallel Optimization Methods for Large Scale Machine Learning. Available: http://arxiv.org/abs/1411.6725v1 DOI: 10.xxxx/xxxx
Richard C. Barnard, Christian Clason (2016). L1 penalization of volumetric dose objectives in optimal control of PDEs. Available...



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