4 papers
A Quadratic-Approximation-Based Stochastic Approximation Method for Weakly Convex Stochastic Programming
Yule Zhang, Benqi Liu, Xiantao Xiao +1
We propose a novel stochastic approximation algorithm, termed PMQSopt, for solving weakly convex stochastic optimization problems involving expectation-valued functions. The algori…
Restarted Reflected Halpern Acceleration for Augmented Primal-Dual Methods
Benqi Liu, Ju Cao, Wotao Yin +1
We study linearly constrained composite convex optimization with a smooth term and a proximable nonsmooth term. We develop a unified augmented primal-dual framework with primal-dua…
A Proximal Augmented Lagrangian Method Based on Quadratic Approximations for Weakly Convex Optimization
Yule Zhang, Benqi Liu, Xiantao Xiao +1
This paper proposes QPALM, a proximal augmented Lagrangian method based on quadratic approximations, for solving nonlinear programming problems with weakly convex objective and con…
An explicit decomposition of higher Deligne-Lsuztig representations
Ben Liu, Sian Nie
In a previous paper, the second named author obtains a decomposition of an elliptic higher Deligne-Lusztig representation into irreducible summands, which are built in the same way…