352 citations · 495 across the 43 of their papers we have counts for
8 papers · 1 filter
A Stochastic Trust-Region Framework for Policy Optimization
Mingming Zhao, Yongfeng Li, Zaiwen Wen
In this paper, we study a few challenging theoretical and numerical issues on the well known trust region policy optimization for deep reinforcement learning. The goal is to find a…
A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization
Minghan Yang, Andre Milzarek, Zaiwen Wen +1
In this paper, a novel stochastic extra-step quasi-Newton method is developed to solve a class of nonsmooth nonconvex composite optimization problems. We assume that the gradient o…
On The Geometric Analysis of A Quartic-quadratic Optimization Problem under A Spherical Constraint
Haixiang Zhang, Andre Milzarek, Zaiwen Wen +1
This paper considers the problem of solving a special quartic-quadratic optimization problem with a single sphere constraint, namely, finding a global and local minimizer of $\frac…
Ground states and their characterization of spin- Bose-Einstein condensates
Tonghua Tian, Yongyong Cai, Xinming Wu +1
The computation of the ground states of spin- Bose-Einstein condensates (BECs) can be formulated as an energy minimization problem with two quadratic constraints. We discretize…
An exact penalty approach for optimization with nonnegative orthogonality constraints
Bo Jiang, Xiang Meng, Zaiwen Wen +1
Optimization with nonnegative orthogonality constraints has wide applications in machine learning and data sciences. It is NP-hard due to some combinatorial properties of the const…
A Brief Introduction to Manifold Optimization
Jiang Hu, Xin Liu, Zaiwen Wen +1
Manifold optimization is ubiquitous in computational and applied mathematics, statistics, engineering, machine learning, physics, chemistry and etc. One of the main challenges usua…
The Search direction Correction makes first-order methods faster
Yifei Wang, Zeyu Jia, Zaiwen Wen
The so-called fast inertial relaxation engine is a first-order method for unconstrained smooth optimization problems. It updates the search direction by a linear combination of the…
Low-rank Matrix Optimization Using Polynomial-filtered Subspace Extraction
Yongfeng Li, Haoyang Liu, Zaiwen Wen +1
In this paper, we study first-order methods on a large variety of low-rank matrix optimization problems, whose solutions only live in a low dimensional eigenspace. Traditional firs…