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20212026
most citedSingle-Leader-Multiple-Followers Stackelberg Security Game with Hypergame Framework

41 citations · 84 across the 20 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

math.OC2023

Non-convex potential games for finding global solutions to sensor network localization

Gehui Xu, Guanpu Chen, Yiguang Hong +3

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and th…

cs.GT2023★ 3 cited

Equalizer zero-determinant strategy in discounted repeated Stackelberg asymmetric game

Zhaoyang Cheng, Guanpu Chen, Yiguang Hong

This paper focuses on the performance of equalizer zero-determinant (ZD) strategies in discounted repeated Stackerberg asymmetric games. In the leader-follower adversarial scenario…

math.OC2023

Riemannian Optimistic Algorithms

Xi Wang, Deming Yuan, Yiguang Hong +3

In this paper, we consider Riemannian online convex optimization with dynamic regret. First, we propose two novel algorithms, namely the Riemannian Online Optimistic Gradient Desce…

cs.GT2023★ 3 cited

Online Game with Time-Varying Coupled Inequality Constraints

Min Meng, Xiuxian Li, Yiguang Hong +2

In this paper, online game is studied, where at each time, a group of players aim at selfishly minimizing their own time-varying cost function simultaneously subject to time-varyin…

math.OC2023★ 1 cited

Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater's Condition

Xinlei Yi, Xiuxian Li, Tao Yang +4

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portio…

math.OC2023★ 1 cited

Distributed Non-Bayesian Learning for Games with Incomplete Information

Shijie Huang, Jinlong Lei, Yiguang Hong

We consider distributed learning problem in games with an unknown cost-relevant parameter, and aim to find the Nash equilibrium while learning the true parameter. Inspired by the s…