Formalization of Complexity Analysis of the First-order Algorithms for Convex Optimization
<|reference_start|>Formalization of Complexity Analysis of the First-order Algorithms for Convex Optimization: The convergence rate of various first-order optimization algorithms is a pivotal concern within the numerical optimization community, as it directly reflects the efficiency of these algorithms across different optimization problems. Our goal is making a significant step forward in the formal mathematical representation of optimization techniques using the Lean4 theorem prover. We first formalize the gradient for smooth functions and the subgradient for convex functions on a Hilbert space, laying the groundwork for the accurate formalization of algorithmic structures. Then, we extend our contribution by proving several properties of differentiable convex functions that have not yet been formalized in Mathlib. Finally, a comprehensive formalization of these algorithms is presented. These developments are not only noteworthy on their own but also serve as essential precursors to the formalization of a broader spectrum of numerical algorithms and their applications in machine learning as well as many other areas.<|reference_end|>
@article{li2024formalization,
    title={Formalization of Complexity Analysis of the First-order Algorithms for
  Convex Optimization},
    author={Chenyi Li, Ziyu Wang, Wanyi He, Yuxuan Wu, Shengyang Xu and Zaiwen Wen},
    journal={arXiv preprint arXiv:2403.11437},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11437},
    primaryClass={math.OC cs.NA math.NA}
}