Grant number: DP210102454 | Funding period: 2021 - 2023
Completed
A Wu, IR Petersen, V Ugrinovskii, I Shames
2025-01-01
In this paper, we develop an online optimization algorithm for solving a class of nonconvex optimization problems with a linearly ..
AX Wu, IR Petersen, I Shames
In this paper, we develop an online optimization algorithm with integral action for solving online optimization problems character..
Y Chen, TL Molloy, T Summers, I Shames
We propose a new framework for solving online linear quadratic (LQ) control problems with time-varying cost matrices that are know..
Z Peng, F Farokhi, Y Pu
In this paper, we study distributed optimization with smooth non-convex local objectives. We propose a novel variant of the well-k..
Y Lin, I Shames, D Nešić
2024-07-01
This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coo..
B Mafakheri, JH Manton, I Shames
2024-01-01
This paper tackles the challenge of decentralised, nonconvex optimisation in situations where agents work asynchronously. Our main..
AX Wu, IR Petersen, V Ugrinovskii, I Shames
In this paper, we extend the recently developed generalized heavy ball optimization algorithm by introducing an additional paramet..
The success of deep learning over the past decade mainly relies on gradient-based optimisation and backpropagation. This paper foc..
V Ugrinovskii, IR Petersen, I Shames
2023-09-01
Among first order optimization methods, Polyak's heavy ball method has long been known to guarantee the asymptotic rate of converg..
Farhad Farokhi
2023-01-01
Stochastic programs, where uncertainty distribution must be inferred from noisy data samples, are considered. They are approximate..
This letter addresses the problem of nonconvex nonsmooth decentralised optimisation in multi-agent networks with undirected connec..
AS Leong, M Zamani, I Shames
2022-01-01
In this letter, we consider the problem of field estimation using binary measurements. Previous work has formulated the problem as..
In this letter we revisit the famous heavy ball method and study its global convergence for a class of non-convex problems with se..
AI Maass, C Manzie, D Nešić, JH Manton, I Shames
We study numerical optimization algorithms that use zeroth-order information to minimize time-varying geodesically convex cost fun..