23 citations · 42 across the 14 of their papers we have counts for
15 papers
A Projection Anderson Acceleration Algorithm for Non-quasi-monotone Variational Inequalities with Hölder Continuous Terms
Xiaojun Chen, C. T. Kelley, Lei Wang
This paper proposes a Projection Anderson Acceleration (PAA) algorithm for variational inequality (VI) problems, where the underlying function is expressed as a finite sum of Hölde…
An Adaptive Smoothing Algorithm for Non-Lipschitz Optimization on Manifolds with Complexity Guarantees
Lei Wang, Xiaojun Chen
We study a class of optimization problems on Riemannian manifolds, where the objective function consists of a smooth term and quasi-norm type penalties with exponent …
Complexity of Projected Gradient Methods for Strongly Convex Optimization with Hölder Continuous Gradient Terms
Xiaojun Chen, C. T. Kelley, Lei Wang
This paper studies the complexity of projected gradient descent methods for a class of strongly convex constrained optimization problems where the objective function is expressed a…
A Support-Set Algorithm for Optimization Problems with Nonnegative and Orthogonal Constraints
Lei Wang, Xin Liu, Xiaojun Chen
In this paper, we investigate optimization problems with nonnegative and orthogonal constraints, where any feasible matrix of size exhibits a sparsity pattern such tha…
A New Complexity Result for Strongly Convex Optimization with Locally -H{ö}lder Continuous Gradients
Xiaojun Chen, C. T. Kelley, Lei Wang
In this paper, we present a new complexity result for the gradient descent method with an appropriately fixed stepsize for minimizing a strongly convex function with locally -H{…
Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances
Lei Wang, Xin Liu, Xiaojun Chen
We consider the distributionally robust optimization (DRO) model of principal component analysis (PCA) to account for uncertainty in the underlying probability distribution. The re…
A Double Tracking Method for Optimization with Decentralized Generalized Orthogonality Constraints
Lei Wang, Nachuan Xiao, Xin Liu
In this paper, we consider the decentralized optimization problems with generalized orthogonality constraints, where both the objective function and the constraint exhibit a distri…
A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds
Lei Wang, Le Bao, Xin Liu
This paper focuses on minimizing a smooth function combined with a nonsmooth regularization term on a compact Riemannian submanifold embedded in the Euclidean space under a decentr…
An Inexact Preconditioned Zeroth-order Proximal Method for Composite Optimization
Shanglin Liu, Lei Wang, Nachuan Xiao +1
In this paper, we consider the composite optimization problem, where the objective function integrates a continuously differentiable loss function with a nonsmooth regularization t…
Smoothing Gradient Tracking for Decentralized Optimization over the Stiefel Manifold with Non-smooth Regularizers
Lei Wang, Xin Liu
Recently, decentralized optimization over the Stiefel manifold has attacked tremendous attentions due to its wide range of applications in various fields. Existing methods rely on…
A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
Lei Wang, Xin Liu
Decentralized optimization with orthogonality constraints is found widely in scientific computing and data science. Since the orthogonality constraints are nonconvex, it is quite c…
Decentralized Optimization Over the Stiefel Manifold by an Approximate Augmented Lagrangian Function
Lei Wang, Xin Liu
In this paper, we focus on the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network of agents. The objective is an average of $…
A Communication-Efficient and Privacy-Aware Distributed Algorithm for Sparse PCA
Lei Wang, Xin Liu, Yin Zhang
Sparse principal component analysis (PCA) improves interpretability of the classic PCA by introducing sparsity into the dimension-reduction process. Optimization models for sparse…
Seeking Consensus on Subspaces in Federated Principal Component Analysis
Lei Wang, Xin Liu, Yin Zhang
In this paper, we develop an algorithm for federated principal component analysis (PCA) with emphases on both communication efficiency and data privacy. Generally speaking, federat…
Multipliers Correction Methods for Optimization Problems over the Stiefel Manifold
Lei Wang, Bin Gao, Xin Liu
We propose a class of multipliers correction methods to minimize a differentiable function over the Stiefel manifold. The proposed methods combine a function value reduction step w…