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New theoretical lower bound established for COCO algorithm

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Researchers have established a new theoretical lower bound for the OGD+Projection algorithm in constrained online convex optimization. This work demonstrates that the cumulative constraint violation (CCV) for the OGD+Projection algorithm is $\Omega (T^{\frac{d-1}{2d}})$, which is the first such lower bound result. This finding is significant as it provides a theoretical limit on the algorithm's performance in scenarios involving convex loss and constraint functions. AI

IMPACT Establishes a theoretical limit for optimization algorithms used in machine learning.

RANK_REASON The item is a research paper detailing a theoretical lower bound for an algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New theoretical lower bound established for COCO algorithm

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The item is a research paper detailing a theoretical lower bound for an algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Haricharan Balasundaram, Karthick Krishna Mahendran, Rahul Vaze ·

    Lower Bound on the Cumulative Constrained Violation for the OGD+Projection algorithm for Constrained Online Convex Optimization (COCO)

    arXiv:2607.10808v1 Announce Type: new Abstract: The problem of constrained online convex optimization is considered, where at each round, once a learner commits to an action $x_t \in \mathcal{X} \subset \mathbb{R}^d$, a convex loss function $f_t$ and a convex constraint function …

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