Wu, AlexPetersen, Ian R.Ugrinovskii, ValeryShames, Iman2026-06-262026-06-26979-8-3503-6761-497983315693720743-1619ORCID:/0000-0003-4856-9450/work/218610800https://hdl.handle.net/1885/733812054In this paper, we develop an online optimization algorithm for solving a class of nonconvex optimization problems with a linearly varying optimal point. The global convergence of the algorithm is guaranteed using the circle criterion for the class of functions whose gradient is bounded within a sector. Also, we show that the corresponding Luré-type nonlinear system involves a double integrator, which demonstrates its ability to track a linearly varying optimal point with zero steady-state error. The algorithm is applied to solving a time-of-arrival based localization problem with constant velocity and the results show that the algorithm is able to estimate the source location with zero steady-state error.This work was supported by the Australian Research Council under grants DP200102945, DP210102454 and DP230102443.5enPublisher Copyright: © 2025 AACC.202510.23919/ACC63710.2025.11108020105015657773