Mathematical Formalism and Algorithmic Advancements in Neural Architecture Search for Asymptotically Optimal Deep Learning Model Convergence

Eilif Schramm

Authors

Eilif Schramm France Author

Keywords:

Neural Architecture Search (NAS), Asymptotic Convergence, Deep Learning, Optimization, Algorithmic Efficiency, Mathematical Formalism, Reinforcement Learning, Evolutionary Algorithms

Abstract

Neural Architecture Search (NAS) has emerged as a powerful technique for automating deep learning model design, significantly improving performance while reducing human intervention. This paper explores the mathematical underpinnings of NAS, focusing on algorithmic advancements aimed at achieving asymptotically optimal model convergence. We discuss various optimization strategies, search spaces, and efficiency improvements, including reinforcement learning, evolutionary algorithms, and differentiable search methods. Our review of literature provides insights into the evolution of NAS, highlighting key theoretical and practical contributions. Furthermore, we propose a formalized mathematical framework to analyze the convergence properties of NAS algorithms and evaluate their real-world applications. Experimental results, visualizations, and case studies are provided to substantiate the theoretical claims.

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