11 papers
Hyperspectral Anomaly Detection via Group Sparse Low-Rank Tensor Factorization With Automatic Anomaly Grouping
Quan Yu, Yu-Hong Dai, Xiongjun Zhang
Low-rank tensor modeling has become an effective tool for hyperspectral anomaly detection. However, existing methods still suffer from high computational cost and limited flexibili…
A Multi-View Coupled Tensor Decomposition for Lightweight Online Adaptive Traffic Prediction
Quan Yu, Jie Ni, Yu-Hong Dai +1
Accurate online traffic prediction is essential for intelligent transportation systems, where forecasting must be performed continuously under imperfect sensing conditions. Missing…
A Multiscale Primal-Dual Interior-Point Relaxation Method for Large-Scale Optimal Transport Problems
Shengyu Sun, Rui-Jin Zhang, Ruoyu Diao +1
Large-scale optimal transport (OT) problems involve a vast number of transport variables, leading to prohibitive memory and computational costs. To address these challenges, we pro…
A primal--dual interior-point method for nonsymmetric conic optimization with conjugate-free scaling
Rui-Jin Zhang, Wenhao Fu, Yu-Hong Dai
We develop a primal--dual interior-point method for nonsymmetric conic optimization based on a conjugate-free scaling matrix. The scaling is obtained from a single-secant BFGS upda…
A Surrogate Value Function Formulation for Bilevel Optimization
Mengwei Xu, Yu-Hong Dai, Xin-Wei Liu +1
The value function formulation captures the hierarchical nature of bilevel optimization through the optimal value function of the lower level problem, yet its implicit and nonsmoot…
A Newton Augmented Lagrangian Method for Symmetric Cone Programming with Complexity Analysis
Rui-Jin Zhang, Ruoyu Diao, Xin-Wei Liu +1
Symmetric cone programming covers a broad class of convex optimization problems, including linear programming, second-order cone programming, and semidefinite programming. Although…
Spectral-Spatial Extraction through Layered Tensor Decomposition for Hyperspectral Anomaly Detection
Quan Yu, Yu-Hong Dai, Minru Bai
Low rank tensor representation (LRTR) methods are very useful for hyperspectral anomaly detection (HAD). To overcome the limitations that they often overlook spectral anomaly and r…
Triangle Steepest Descent: A Geometry-Based Gradient Algorithm with Guaranteed R-Linear Convergence
Ya Shen, Qing-Na Li, Yu-Hong Dai
Gradient methods are among the simplest yet most widely used algorithms for unconstrained optimization. Motivated by a geometric property of the steepest descent (SD) method that c…
An Efficient Stochastic Subgradient Method for the Global Placement Problem in Very Large-Scale Integration Circuits
Yi-Shuang Yue, Yu-Hong Dai, Haijun Yu
The placement problem in Very Large-Scale Integration (VLSI) circuits is a critical step in chip design. Its primary goal is to optimize the wirelength of circuit components within…
Enhanced Barrier-Smoothing Technique for Bilevel Optimization with Nonsmooth Mappings
Mengwei Xu, Yu-Hong Dai, Xin-Wei Liu +1
Bilevel optimization problems, encountered in fields such as economics, engineering, and machine learning, pose significant computational challenges due to their hierarchical struc…
Exact penalty method for D-stationary point of nonlinear optimization
Xin-Wei Liu, Yu-Hong Dai
We consider the nonlinear optimization problem with least -norm measure of constraint violations and introduce the concepts of the D-stationary point, the DL-stationary poi…