# Bandit Convex Optimization for Scalable and Dynamic IoT Management

arXiv: https://arxiv.org/abs/1707.09060 · HTML: https://mlsanity.com/paper/1707.09060/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2017.

## Built on by (most cited first, 24 of 24)

- [A Unifying Framework for Online Optimization with Long-Term Constraints](https://mlsanity.com/paper/2209.07454/) (2022)
- [Optimal Algorithms for Online Convex Optimization with Adversarial Constraints](https://mlsanity.com/paper/2310.18955/) (2023)
- [Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization](https://mlsanity.com/paper/2105.00321/) (2021)
- [Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates](https://mlsanity.com/paper/2310.19807/) (2023)
- [Simultaneously Achieving Sublinear Regret and Constraint Violations for Online Convex Optimization with Time-varying Constraints](https://mlsanity.com/paper/2111.07707/) (2021)
- [Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm](https://mlsanity.com/paper/2504.07388/) (2025)
- [Best of Many Worlds Guarantees for Online Learning with Knapsacks](https://mlsanity.com/paper/2202.13710/) (2022)
- [On Dynamic Regret and Constraint Violations in Constrained Online Convex Optimization](https://mlsanity.com/paper/2301.09808/) (2023)
- [Online Convex Optimization with Switching Cost and Delayed Gradients](https://mlsanity.com/paper/2310.11880/) (2023)
- [Monotone Individual Fairness](https://mlsanity.com/paper/2403.06812/) (2024)
- [A New Benchmark for Online Learning with Budget-Balancing Constraints](https://mlsanity.com/paper/2503.14796/) (2025)
- [Lazy Queries Can Reduce Variance in Zeroth-order Optimization](https://mlsanity.com/paper/2206.07126/) (2022)
- [Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems](https://mlsanity.com/paper/2405.19015/) (2024)
- [Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks](https://mlsanity.com/paper/2408.16215/) (2024)
- [Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints](https://mlsanity.com/paper/2505.02640/) (2025)
- [Non-stationary Bandit Convex Optimization: A Comprehensive Study](https://mlsanity.com/paper/2506.02980/) (2025)
- [No-regret learning for repeated non-cooperative games with lossy bandits](https://mlsanity.com/paper/2205.06968/) (2022)
- [Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications](https://mlsanity.com/paper/2306.05655/) (2023)
- [Online Dynamic Submodular Optimization](https://mlsanity.com/paper/2306.10835/) (2023)
- [A conversion theorem and minimax optimality for continuum contextual bandits](https://mlsanity.com/paper/2406.05714/) (2024)
- [Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints](https://mlsanity.com/paper/2510.07266/) (2025)
- [Multi-Objective textitmin-max Online Convex Optimization](https://mlsanity.com/paper/2510.13560/) (2025)
- [Bandits in Flux: Adversarial Constraints in Dynamic Environments](https://mlsanity.com/paper/2601.19867/) (2026)
- [Nonparametric Learning and Earning with One-Point Feedback under Nonstationarity](https://mlsanity.com/paper/2605.21263/) (2026)

## Abstract (arXiv)

The present paper deals with online convex optimization involving both time-varying loss functions, and time-varying constraints. The loss functions are not fully accessible to the learner, and instead only the function values (a.k.a. bandit feedback) are revealed at queried points. The constraints are revealed after making decisions, and can be instantaneously violated, yet they must be satisfied in the long term. This setting fits nicely the emerging online network tasks such as fog computing in the Internet-of-Things (IoT), where online decisions must flexibly adapt to the changing user preferences (loss functions), and the temporally unpredictable availability of resources (constraints). Tailored for such human-in-the-loop systems where the loss functions are hard to model, a family of bandit online saddle-point (BanSaP) schemes are developed, which adaptively adjust the online operations based on (possibly multiple) bandit feedback of the loss functions, and the changing environment. Performance here is assessed by: i) dynamic regret that generalizes the widely used static regret; and, ii) fit that captures the accumulated amount of constraint violations. Specifically, BanSaP is proved to simultaneously yield sub-linear dynamic regret and fit, provided that the best dynamic solutions vary slowly over time. Numerical tests in fog computation offloading tasks corroborate that our proposed BanSaP approach offers competitive performance relative to existing approaches that are based on gradient feedback.