7 citations · 11 across the 4 of their papers we have counts for
6 papers
Smoothed Gradient Method for Nonconvex Federated Stochastic Bilevel Optimization
Xinwen Zhang, Peiran Yu, Zhaosong Lu +1
In recent years, federated stochastic bilevel optimization has attracted increasing attention due to its wide range of applications in machine learning. To reduce the computational…
Convergence analysis for a variant of manifold proximal point algorithm based on Kurdyka-Łojasiewicz property
Peiran Yu, Liaoyuan Zeng, Ting Kei Pong
We incorporate an iteratively reweighted strategy in the manifold proximal point algorithm (ManPPA) in [12] to solve an enhanced sparsity inducing model for identifying sparse yet…
Analysis and algorithms for some compressed sensing models based on L1/L2 minimization
Liaoyuan Zeng, Peiran Yu, Ting Kei Pong
Recently, in a series of papers [32,38,39,41], the ratio of and norms was proposed as a sparsity inducing function for noiseless compressed sensing. In this paper…
Convergence rate analysis of a sequential convex programming method with line search for a class of constrained difference-of-convex optimization problems
Peiran Yu, Ting Kei Pong, Zhaosong Lu
In this paper, we study the sequential convex programming method with monotone line search (SCP) in [46] for a class of difference-of-convex (DC) optimization problems with…
Kurdyka-Łojasiewicz exponent via inf-projection
Peiran Yu, Guoyin Li, Ting Kei Pong
Kurdyka-Lojasiewicz (KL) exponent plays an important role in estimating the convergence rate of many contemporary first-order methods. In particular, a KL exponent of for…
Iteratively reweighted algorithms with extrapolation
Peiran Yu, Ting Kei Pong
Iteratively reweighted algorithm is a popular algorithm for solving a large class of optimization problems whose objective is the sum of a Lipschitz differentiable loss fu…