arXiv:2601.20076v2 Announce Type: replace-cross Abstract: We consider minimizing an objective function subject to constraints defined by the intersection of lower-level sets of convex functions. We study two cases: (i) strongly convex and Lipschitz-smooth objective function and (ii) convex but possibly nonsmooth objective function. To deal with the constraints that are not easy to project on, we use a randomized feasibility algorithm with Polyak steps and a random number of sampled constraints per iteration, while taking (sub)gradient steps to minimize the objective function. For case (i), we
This paper leverages recent advancements in randomized optimization techniques to address complex constraints in AI/ML model training, pushing the frontier of efficient algorithm design.
Improved optimization methods directly enhance the efficiency and capability of AI systems, enabling faster training and deployment of more complex models across various applications.
The proposed adaptive step size and randomized feasibility methods could lead to more robust and computationally less intensive solutions for constrained optimization problems in AI.
- · AI developers
- · Machine learning researchers
- · Industries relying on complex AI models
- · Legacy optimization techniques
More efficient training of large-scale AI models, reducing computational costs and time.
Accelerated development of AI agents and autonomous systems that require complex constraint handling.
Broader adoption of AI in resource-constrained environments due to improved algorithmic efficiency.
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