2 citations · 2 across the 8 of their papers we have counts for
8 papers
Sparse Recovery via Minimization
Lang Yu, Nan-jing Huang
The weighted difference of squared norms (WDSN) penalty with has attracted considerable attention due to its strong sparsity-promoting ability…
Convergence of iterates and improved rates for accelerated augmented Lagrangian methods for linearly constrained convex optimization
Xin He, Nan-Jing Huang, Yi-Bin Xiao +1
Motivated by an inertial primal-dual dynamical system with vanishing damping, we propose a class of accelerated augmented Lagrangian methods with Nesterov extrapolation parameters…
Trajectory convergence and rates for Nesterov accelerated primal-dual dynamics without Lipschitz gradient assumption
Xin He, Nan-Jing Huang, Yi-Bin Xiao +1
We consider the Nesterov accelerated primal-dual dynamical system \[ \begin{cases} \ddot{x}(t)+\dfracα{t}\dot{x}(t) +\nabla f(x(t)) +A^\top\bigl(λ(t)+θt\dotλ(t)\bigr)+βA^\top(Ax(t)…
Sparse Recovery via Ratio Minimization: Theory and Algorithm
Lang Yu, Nan-jing Huang
The constrained ratio model is scale invariant and is therefore attractive for sparse signal recovery. However, its nonconvex, nonsmooth, and fractional structu…
Learning Aligned Stability in Neural ODEs Reconciling Accuracy with Robustness
Chaoyang Luo, Yan Zou, Nanjing Huang
Despite Neural Ordinary Differential Equations (Neural ODEs) exhibiting intrinsic robustness, existing methods often impose Lyapunov stability for formal guarantees. However, these…
FxTS-Net: Fixed-Time Stable Learning Framework for Neural ODEs
Chaoyang Luo, Yan Zou, Wanying Li +1
Neural Ordinary Differential Equations (Neural ODEs), as a novel category of modeling big data methods, cleverly link traditional neural networks and dynamical systems. However, it…
Non-ergodic convergence rate of an inertial accelerated primal-dual algorithm for saddle point problems
X. He, N. J. Huang, Y. P. Fang
In this paper, we design an inertial accelerated primal-dual algorithm to address the convex-concave saddle point problem, which is formulated as $\min_{x}\max_{y} f(x) + \langle K…
Accelerated linearized alternating direction method of multipliers with Nesterov extrapolation
X. He, N. J. Huang, Y. P. Fang
The alternating direction method of multipliers (ADMM) has found widespread use in solving separable convex optimization problems. In this paper, by employing Nesterov extrapolatio…