4 citations · 6 across the 5 of their papers we have counts for
5 papers
Revisiting the Primal-Dual Method of Multipliers for Optimisation over Centralised Networks
Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
The primal-dual method of multipliers (PDMM) was originally designed for solving a decomposable optimisation problem over a general network. In this paper, we revisit PDMM for opti…
Approximated Orthonormal Normalisation in Training Neural Networks
Guoqiang Zhang, Kenta Niwa, W. B. Kleijn
Generalisation of a deep neural network (DNN) is one major concern when employing the deep learning approach for solving practical problems. In this paper we propose a new techniqu…
Rapidly Adapting Moment Estimation
Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
Adaptive gradient methods such as Adam have been shown to be very effective for training deep neural networks (DNNs) by tracking the second moment of gradients to compute the indiv…
On Relationship between Primal-Dual Method of Multipliers and Kalman Filter
Guoqiang Zhang, W. Bastiaan Kleijn, Richard Heusdens
Recently the primal-dual method of multipliers (PDMM), a novel distributed optimization method, was proposed for solving a general class of decomposable convex optimizations over g…
Distributed Optimization Using the Primal-Dual Method of Multipliers
G. Zhang, R. Heusdens
In this paper, we propose the primal-dual method of multipliers (PDMM) for distributed optimization over a graph. In particular, we optimize a sum of convex functions defined over…