5 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 abilit…
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(…
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…