Abstract: As machine learning continues its rapid expansion across diverse domains, the demand for efficient optimization techniques becomes increasingly pressing. In the context of modern machine learning, characterized by exponential growth in data volume and model complexity, traditional optimization methods face new challenges. Addressing these challenges requires a systematic review and summary of optimization methods tailored to the unique demands of machine learning.
This paper presents an overview of optimization problems in the realm of machine learning, focusing on the specific context of Hessian-Free Optimization. We delve into the principles and advancements of commonly employed optimization techniques, highlighting their applicability and limitations within the Hessian-Free framework. Furthermore, we examine the applications and developments of optimization methods in prominent machine learning fields, illustrating the versatility and impact of Hessian-Free Optimization across various domains.
Finally, we discuss the challenges and open problems that lie ahead in the optimization landscape of machine learning, offering insights into future research directions and opportunities for innovation in the context of Hessian-Free Optimization. Through this comprehensive exploration, we aim to provide valuable guidance for both the advancement of optimization methodologies and the evolution of machine learning research.
Keywords: Adaptive Hessian-Free Optimization, Machine Learning Optimization, Large-scale optimization problems, Hessian matrix
|
DOI:
10.17148/IJARCCE.2024.13653
