{
  "abstract": "This paper treats a system that performs back-to-back tasks. Each new task has a random matrix of parameters and can be processed in various processing modes. The processing mode determines the task duration and a vector of rewards (penalties are viewed as negative rewards). The reward vector can include quality, profit, or $-1$ times a penalty such as cost or energy. In wireless applications, tasks correspond to data transmissions and rewards/penalties correspond to throughput and distortion. Prior work treats the problem of maximizing the time average of a reward process subject to constraints. This paper seeks to maximize a concave function of the time average reward vector. This is difficult because it involves a multi-variable function of ratios of averages. We develop a single algorithm that does not know the probability distribution of the random task matrices. The algorithm is adaptive, meaning it responds efficiently (with optimal adaptation time) when probabilities change.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.14297",
  "author_total": 1,
  "authors": [
    {
      "name": "Michael J. Neely"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/46gcjjqjkkesw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.14297",
  "fulltext_url": null,
  "id": "46gcjjqjkkesw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.14297",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2208|Advanced Wireless Network Optimization"
    ]
  ],
  "openalex": "W7221775677",
  "pdf_url": "https://arxiv.org/pdf/2610.14297",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/46gcjjqjkkesw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:15Z",
      "native_id": "2610.14297",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.14297"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Adaptive Utility Optimization for Stochastic Renewal Systems",
  "title_text": "Adaptive Utility Optimization for Stochastic Renewal Systems",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:15Z",
  "url": "https://research.draconitic.com/paper/46gcjjqjkkesw",
  "venue": "arXiv",
  "versions": []
}