Publication

Machine Learning

• X. Wu, H.R. Feyzmahdavian, S. Magnusson, M. Johansson.
  Delay-adaptive Step-sizes for Asynchronous Learning.
  Proc. International Conference on Machine Learning (ICML), 2022.

• X. Wu, C. Liu, S. Magnusson, M. Johansson.
  Delay-agnostic Asynchronous Coordinate Update Algorithm.
  Proc. International Conference on Machine Learning (ICML), 2023.

Multi-agent Optimization

Preprints

• C. Liu, X. Tan, X. Wu, D. V. Dimarogonas, K. H. Johansson.
  Achieving Violation-free Distributed Optimization under Coupling Constraints, 2024.
  

• X. Wu, C. Liu, S. Magnusson, M. Johansson.
  Asynchronous Distributed Optimization with Delay-free Parameters, 2023.
  

Journal Papers

• X. Wu, K. Sou, and J. Lu.
  A Regularized Fenchel Dual Gradient Method for Nonsmooth Optimization over Time-varying Networks.
  Optimization Methods and Software, 2023.

• X. Wu, S. Magnusson, and M. Johansson.
  Distributed Safe Resource Allocation using Barrier Functions
  Automatica, 2023.

• X. Wu, H. Wang, and J. Lu.
  Distributed Optimization with Coupling Constraints.
  IEEE Transactions on Automatic Control, 2023.

• X. Wu and J. Lu.
  A Unifying Approximate Method of Multipliers for Distributed Composite Optimization.
  IEEE Transactions on Automatic Control, 2023.

• H. Wei, Z. Qu, X. Wu, H. Wang, and J. Lu.
  Decentralized Approximate Newton Methods for Convex Optimization on Networked Systems.
  IEEE Transactions on Control of Network Systems, vol. 8, no. 3, pp. 1489-1500, 2021.

• X. Wu, Z. Qu, and J. Lu.
  A Second-Order Proximal Algorithm for Consensus Optimization.
  IEEE Transactions on Automatic Control, vol. 66, no. 4, pp. 1864-1871, 2021.

• X. Wu and J. Lu.
  Distributed Optimization over Time-varying Networks with Minimal Connectivity.
  IEEE Control Systems Letters, vol. 4, no. 3, pp. 536–541, 2020.

• X. Wu and J. Lu.
  Fenchel Dual Gradient Methods for Distributed Convex Optimization over Time-varying Networks.
  IEEE Transactions on Automatic Control, vol. 64, no. 11, pp. 4629–4636, 2019.

Conference Papers

• X. Wu, C. Liu, S. Magnusson, M. Johansson.
  Delay-agnostic Asynchronous Distributed Optimization.
  Proc. IEEE Conference on Decision and Control (invited), 2023.

• E. Berglund, S. Khirirat, X. Wu, S. Magnusson, and M. Johansson.
  Revisiting the Curvature-aided IAG: Improved Theory and Reduced Complexity.
  Proc. IFAC World Congress, 2023.

• C. Liu, X. Wu, X. Yi, Y. Shi, K. H. Johansson.
  Rate analysis of dual averaging for nonconvex distributed optimization.
  Proc. IFAC World Congress, 2023.

• X. Wu, H.R. Feyzmahdavian, S. Magnusson, M. Johansson.
  Optimal convergence rates of totally asynchronous optimization.
  Proc. IEEE Conference on Decision and Control, pp. 6484-6490, Cancun, Mexico, 2022.

• X. Wu, S. Magnusson, M. Johansson.
  A New Family of Feasible Methods for Distributed Resource Allocation.
  Proc. IEEE Conference on Decision and Control (invited), pp. 3355-3360, Austin, Texas, 2021.

• X. Wu, H. Wang, and J. Lu.
  A Distributed Proximal Primal-Dual Algorithm for Nonsmooth Optimization with Coupling Constraints.
  Proc. IEEE Conference on Decision and Control (invited), pp. 3657-3662, Jeju Island, Korea, 2020.

• X. Wu and J. Lu.
  Improved Convergence Rates of P-EXTRA for Non-smooth Distributed Optimization.
  Proc. IEEE International Conference on Control & Automation (invited), pp. 55–60, Edinburgh, Scotland, 2019. (Best Student Paper Finalist)

• Z. Qu, X. Wu, and J. Lu.
  Finite-Time-Consensus-Based Methods for Distributed Optimization.
  Proc. Chinese Control Conference (invited), pp. 5764–5769, Guangzhou, China, 2019.

• X. Wu, K. C. Sou, and J. Lu.
  Fenchel Dual Gradient Methods Enabling a Smoothing Technique for Nonsmooth Distributed Convex Optimization.
  Proc. IEEE Conference on Decision and Control (invited), pp. 1757–1762, Miami, FL, 2018.

• H. Wei, Z. Qu, X. Wu, H. Wang, and J. Lu.
  An Approximately-Zero-Gradient-Sum Algorithm for Consensus Optimization.
  Proc. International Conference on Control, Automation, Robotics and Vision (invited), pp. 826–830, Singapore, Singapore, 2018.

• X. Wu and J. Lu.
  Deterministic Coordinate Descent Algorithms for Smooth Convex Optimization.
  Proc. IEEE Conference on Decision and Control, pp. 709–714, Melbourne, Australia, 2017.

• X. Wu and J. Lu.
  Fenchel Dual Gradient Methods for Distributed Convex Optimization over Time-varying Networks.
  Proc. IEEE Conference on Decision and Control, pp. 2894–2899, Melbourne, Australia, 2017.