Adaptive Utility Optimization for Stochastic Renewal Systems
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.
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BibTeX
@misc{neely2026adaptive,
title = {{Adaptive Utility Optimization for Stochastic Renewal Systems}},
author = {Michael J. Neely},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.14297},
eprint = {2610.14297},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.14297},
}
RIS
TY - UNPB
TI - Adaptive Utility Optimization for Stochastic Renewal Systems
AU - Michael J. Neely
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.14297
UR - https://arxiv.org/abs/2610.14297
AB - 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.
ER -