7 citations · 28 across the 11 of their papers we have counts for
11 papers
A Surrogate model for High Temperature Superconducting Magnets to Predict Current Distribution with Neural Network
Mianjun Xiao, Peng Song, Yulong Liu +7
Finite element methods (FEM) for high-temperature superconducting (HTS) magnets become time-consuming at larger scales, restricting the rapid optimization of meter-scale REBCO sole…
GBM-based Bregman Proximal Algorithms for Constrained Learning
Zhenwei Lin, Qi Deng
As the complexity of learning tasks surges, modern machine learning encounters a new constrained learning paradigm characterized by more intricate and data-driven function constrai…
A Homogenization Approach for Gradient-Dominated Stochastic Optimization
Jiyuan Tan, Chenyu Xue, Chuwen Zhang +3
Gradient dominance property is a condition weaker than strong convexity, yet sufficiently ensures global convergence even in non-convex optimization. This property finds wide appli…
An Enhanced ADMM-based Interior Point Method for Linear and Conic Optimization
Qi Deng, Qing Feng, Wenzhi Gao +8
The ADMM-based interior point (ABIP, Lin et al. 2021) method is a hybrid algorithm that effectively combines interior point method (IPM) and first-order methods to achieve a perfor…
Level Constrained First Order Methods for Function Constrained Optimization
Digvijay Boob, Qi Deng, Guanghui Lan
We present a new feasible proximal gradient method for constrained optimization where both the objective and constraint functions are given by the summation of a smooth, possibly n…
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization
Qi Deng, Wenzhi Gao
Stochastic model-based methods have received increasing attention lately due to their appealing robustness to the stepsize selection and provable efficiency guarantee. We make two…
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
Digvijay Boob, Qi Deng, Guanghui Lan +1
Nonconvex sparse models have received significant attention in high-dimensional machine learning. In this paper, we study a new model consisting of a general convex or nonconvex ob…
Efficiency of Coordinate Descent Methods For Structured Nonconvex Optimization
Qi Deng, Chenghao Lan
Novel coordinate descent (CD) methods are proposed for minimizing nonconvex functions consisting of three terms: (i) a continuously differentiable term, (ii) a simple convex term,…
Stochastic First-order Methods for Convex and Nonconvex Functional Constrained Optimization
Digvijay Boob, Qi Deng, Guanghui Lan
Functional constrained optimization is becoming more and more important in machine learning and operations research. Such problems have potential applications in risk-averse machin…
Optimal Adaptive and Accelerated Stochastic Gradient Descent
Qi Deng, Yi Cheng, Guanghui Lan
Stochastic gradient descent (\textsc{Sgd}) methods are the most powerful optimization tools in training machine learning and deep learning models. Moreover, acceleration (a.k.a. mo…
Randomized Block Subgradient Methods for Convex Nonsmooth and Stochastic Optimization
Qi Deng, Guanghui Lan, Anand Rangarajan
Block coordinate descent methods and stochastic subgradient methods have been extensively studied in optimization and machine learning. By combining randomized block sampling with…