arXiv:2606.05380v1 Announce Type: cross Abstract: We present learning-augmented algorithms for two general classes of online minimization problems: metrical task systems and laminar set cover. Both algorithms achieve improved theoretical guarantees using machine-learned predictions of an optimal solution to the dual linear program. Unlike optimal primal solutions, which can change drastically under tiny instance perturbations, these dual solutions are much more stable, which ensures the existence of good (and learnable) predictions for families of similar instances. While previous work has use
This research addresses a fundamental challenge in online algorithms by leveraging machine-learned predictions, which is increasingly viable due to advancements in AI and data availability.
Improved online minimization algorithms with robust prediction capabilities can drastically enhance efficiency and resource allocation in dynamic, real-world systems, leading to more resilient and adaptive automated decision-making.
The focus on stable dual solutions for predictions offers a more reliable pathway to integrate machine learning into online algorithms, potentially enabling more practical and effective 'learning-augmented' systems than prior works.
- · AI researchers and developers
- · Logistics and supply chain management
- · Cloud computing providers
- · Robotics and automation
- · Traditional online algorithm approaches
- · Systems highly sensitive to prediction errors
The immediate impact is more efficient and adaptable online algorithms across various applications.
This could lead to automated systems that make better real-time decisions in complex, uncertain environments, reducing operational costs and improving performance.
Long-term, highly reliable and scalable learning-augmented algorithms might accelerate the deployment and capability of advanced AI agents in dynamic settings, impacting white-collar and industrial automation significantly.
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Read at arXiv cs.LG