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Online Combinatorial Linear Optimization via a Frank-Wolfe-based Metarounding Algorithm

arXiv 2310.12629 ↗First posted 19 Oct 2023cs.DS cs.LGcited by 0

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Abstract (arXiv)

Metarounding is an approach to convert an approximation algorithm for linear optimization over some combinatorial classes to an online linear optimization algorithm for the same class. We propose a new metarounding algorithm under a natural assumption that a relax-based approximation algorithm exists for the combinatorial class. Our algorithm is much more efficient in both theoretical and practical aspects.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 31 references that are in the map. First posted on arXiv 19 Oct 2023.

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4 of 31 references.

References · 4

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2019Introduction to Online Convex Optimizationcited by 803
  3. 2017Efficient Online Linear Optimization with Approximation Algorithmscited by 12
  4. 2022Boosting as Frank-Wolfecited by 2

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