Tracking Moving Agents via Inexact Online Gradient Descent Algorithm
By Amrit Singh Bedi, Paban Sarma, Ketan Rajawat. 2017
45 citations recorded by the source corpus.
Multiagent systems are being increasingly deployed in challenging environments for performing complex tasks such as multitarget tracking, search-and-rescue, and intrusion detection. Not with standing 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 nonstrongly convex costs but also explain 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 multiagent multitarget tracking problem, while its flexibility is exemplified by generating online movie recommendations for Movielens 10M dataset.
Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research
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Citations in the corpus
- Decentralized Online Learning: Take Benefits from Others’ Data without Sharing Your Own to Track Global Trend (2019, neutral): “[3, 16, 26, 39, 44, 45] chooses the reference points {y∗ � }�=1 with y∗ � = argminz∈X �� (z), where �� is the loss function at the �-th iteration.”
- Proximal Online Gradient Is Optimum for Dynamic Regret: A General Lower Bound (2021, background): “When querying noisy gradient, [30] obtains O (D + E) regret, where E is the cumulative gradient error.”
- Proximal Online Gradient is Optimum for Dynamic Regret (2018, background): “When querying noisy gradient, Bedi et al. [2018] obtains O (D(T ) + E(T )) regret, where E(T ) is the cumulative gradient error. Yang et al. [2016], Gao et al. [2018] extends it for non-strongly convex and non-convex functions, respectively.”