7 papers · 1 filter
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…
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…