Multiple-gradient descent algorithm (MGDA) for multiobjective optimization. Comptes Rendus Mathematique Tome 350, Fascicule 5-6 (Mar. 2012), 313-318.Desideri JA. Multiple-gradient descent algorithm (MGDA) for m
D\acute{e}sid\acute{e}ri J.-A. Multiple-gradient descent algorithm (MGDA) for multiobjective optimization. Comptes Rendus Mathematique, vol. 350, pp. 313–318, 2012.概本文尝试同时解决 nn 个任务: Ji(θ),i=1,2,⋯,nJi(θ),i=1,2,⋯,n, 其中 θ∈RN,n≤Nθ∈RN,n≤N....
This article compounds and extends several publications in which a Multiple-Gradient Descent Algorithm (MGDA), has been proposed and tested for the treatment of multi-objective differentiable optimization. Originally introduced in [3], the method has bee
which allows the sampled particles to move to the joint high-likelihood region of the target distributions. Interestingly, the asymptotic analysis shows that our approach reduces exactly to the multiple-gradient descent algorithm for multi-objective optimization, as expected. Finally, we conduct comprehen...
An accelerated distributed algorithm combining the Nesterov acceleration and gradient tracking with constant step sizes is proposed for seeking the NE of multiple cluster games over time-varying unbalanced digraphs, which converges faster than existing results (Meng and Li, 2023, Zhou et al., 2023) ...
Parametric Optimization of Pulsating Jets in Unsteady Flow by Multiple-Gradient Descent Algorithm (MGDA)Two numerical methodologies are combined to optimize six design characteristics of a system of pulsating jets acting on a laminar boundary layer governed by the compressible Navier-Stokes equations......
In this paper, we develop a novel Accelerated Gradient-descent Multiple Access (AGMA) algorithm that uses momentum-based gradient signals over noisy fading MAC to improve the convergence rate as compared to existing methods. Furthermore, AGMA does not require power control or beamforming to cancel...
The network model is a dynamical system in which the transition function is a contraction mapping, and the learning algorithm is based on the gradient descent method. We show a numerical simulation in which a recurrent neural network obtains a multiple periodic attractor consisting of five Lissajous...
The convergence rate and estimation performance of the proposed method can be significantly improved, since the steepest descent step and Barzilai-Borwein step are alternately used as the search step in the unconstrained convex optimization. The proposed method can obtain satisfactory performance especially...
In response to this limitation, our research investigates a hybrid beamforming approach, seeking to optimize spectral efficiency through an alternating optimization (AO) technique. Our goal is to develop an algorithm that can be easily integrated into diverse hybrid beamforming configurations. On the ...