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Dynamic Optimistic Constrained OCO with Memory via Delay Equivalence

Nov 5, 2026 Source ↗

Key Insights

Online Convex Optimization with memory and time-varying constraints (COCO-M) is a recently-introduced framework that captures the dynamics of stateful online learning and non-stochastic control with b.

Cite this synthesis DOI: 10.48550/arXiv.2611.08302 ↗

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@misc{ acaciadata-research-dynamic-optimistic-constrained-oco-with-memory-via-delay-equ,
  title = { Dynamic Optimistic Constrained OCO with Memory via Delay Equivalence },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2611.08302 },
  url = { http://arxiv.org/abs/2611.08302v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - Dynamic Optimistic Constrained OCO with Memory via Delay Equivalence
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2611.08302
UR  - http://arxiv.org/abs/2611.08302v1
ER  -

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Overview

Online Convex Optimization with memory and time-varying constraints (COCO-M) is a recently-introduced framework that captures the dynamics of stateful online learning and non-stochastic control with b

Key Topics

Stream Processing: A theme covered by this item within the data engineering context.

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Article Metadata

Evergreen Synthesized

dataops stream-processing

Stream Processing DataOps

SQI 0.750