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New algorithm achieves $O(d)$ regret in online inverse optimization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new deterministic algorithm for online inverse linear optimization that achieves $O(d)$ regret, a significant improvement over previous methods. This algorithm operates efficiently with $O(d^2)$ time per round, making it practical for use. The work builds upon the variable-metric framework and introduces a novel self-normalized rank-one update, replacing the logarithmic determinant potential with a trace power function for improved bounds. AI

IMPACT This theoretical advancement in optimization algorithms could lead to more efficient AI model training and inference in the future.

RANK_REASON The cluster contains a research paper detailing a new algorithm with theoretical performance improvements. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm achieves $O(d)$ regret in online inverse optimization

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The cluster contains a research paper detailing a new algorithm with theoretical performance improvements. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yang Cai, Anupam Gupta, Vineet Gupta, Guru Guruganesh, Yanchen Jiang, Christopher Liaw, Aranyak Mehta, Renato Paes Leme, Grigoris Velegkas, Di Wang ·

    Efficient Online Inverse Optimization with $O(d)$ Regret

    arXiv:2609.13440v1 Announce Type: new Abstract: We give a deterministic algorithm for online inverse linear optimization with regret $O(d)$, uniform in the horizon and $O(d^{2})$ time per round. A bound of this order was obtained recently by Dewasurendra, settling a question of G…

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