# Tracking Moving Agents via Inexact Online Gradient Descent Algorithm

arXiv: https://arxiv.org/abs/1710.05133 · HTML: https://mlsanity.com/paper/1710.05133/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2017.

## Built on by (most cited first, 6 of 6)

- [A CMDP-within-online framework for Meta-Safe Reinforcement Learning](https://mlsanity.com/paper/2405.16601/) (2024)
- [Particle-based Online Bayesian Sampling](https://mlsanity.com/paper/2302.14796/) (2023)
- [Learning Rate Schedules in the Presence of Distribution Shift](https://mlsanity.com/paper/2303.15634/) (2023)
- [Distributionally Time-Varying Online Stochastic Optimization under Polyak-Lojasiewicz Condition with Application in Conditional Value-at-Risk Statistical Learning](https://mlsanity.com/paper/2309.09411/) (2023)
- [Optimized Gradient Tracking for Decentralized Online Learning](https://mlsanity.com/paper/2306.06375/) (2023)
- [Efficient Hyperparameter Search for Non-Stationary Model Training](https://mlsanity.com/paper/2512.01258/) (2025)

## Abstract (arXiv)

Multi-agent systems are being increasingly deployed in challenging environments for performing complex tasks such as multi-target tracking, search-and-rescue, and intrusion detection. Notwithstanding the computational limitations of individual robots, such systems rely on collaboration to sense and react to the environment. This paper formulates the generic target tracking problem as a time-varying optimization problem and puts forth an inexact online gradient descent method for solving it sequentially. The performance of the proposed algorithm is studied by characterizing its dynamic regret, a notion common to the online learning literature. Building upon the existing results, we provide improved regret rates that not only allow non-strongly convex costs but also explicating the role of the cumulative gradient error. Two distinct classes of problems are considered: one in which the objective function adheres to a quadratic growth condition, and another where the objective function is convex but the variable belongs to a compact domain. For both cases, results are developed while allowing the error to be either adversarial or arising from a white noise process. Further, the generality of the proposed framework is demonstrated by developing online variants of existing stochastic gradient algorithms and interpreting them as special cases of the proposed inexact gradient method. The efficacy of the proposed inexact gradient framework is established on a multi-agent multi-target tracking problem, while its flexibility is exemplified by generating online movie recommendations for Movielens 10M dataset.