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Publications of Xin Liu
Published Papers and Preprints
T. Gu et al., HiMOSS: A Novel High-dimensional Multi-objective Optimization Method via Adaptive Gradient-Based Subspace Sampling for Analog Circuit Sizing, DAC ’24: Proceedings of the 61st ACM/IEEE Design Automation Conference, 6 (2024), 1-6. link
L. Wang, N. Xiao and X. Liu, A Double Tracking Method for Optimization with Decentralized Generalized Orthogonality Constraints (under review). link
Y. Hu, J. Yin, X. Gao, X. Liu and H. Song, Projected gradient descent algorithm for ab initio crystal structure relaxation under a fixed unit cell volume, Physical Review B, 109(2024), 224108. link
S. Zhang, N, Xiao and X. Liu, Decentralized Stochastic Subgradient Methods for Nonsmooth Nonconvex Optimization (under review). link
L. Wang, L. Bao and X. Liu, A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds (under review). link
S. Liu, L. Wang, N. Xiao and X. Liu, An Inexact Preconditioned Zeroth-order Proximal Method for Composite Optimization (under review). link
X. Hu, X. Liu and N. Xiao, Convergence Properties of Stochastic Proximal Subgradient Method in Solving a Class of Composite Optimization Problems with Cardinality Regularizer, Journal of Industrial and Management Optimization, 20-5 (2024), 1934-1950. link
T. Gu et al., BBGP-sDFO: Batch Bayesian and Gaussian Process Enhanced Subspace Derivative Free Optimization for High-Dimensional Analog Circuit Synthesis, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 43-2 (2023), 417-430. link
Y. Hu, M. Li, X. Liu and C. Meng, Sampling-Based Approaches for Multimarginal Optimal Transport Problems with Coulomb Cost, Mathematics of Computation (2024). link
X. Liu, Optimization Models and Approaches for Strongly Correlated Electrons Systems, Mathematica Numerica Sinica (in Chinese), 45-2 (2023), 141-159. link
N. Xiao, X. Hu, X. Liu and K. Toh, Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees, Journal of Machine Learning Research, 25-48 (2024), 1-53. link
N. Xiao, X. Liu and K. Toh, A Partial Exact Penalty Function Approach for Constrained Optimization (under review). link
L. Wang and X. Liu, Smoothing Gradient Tracking for Decentralized Optimization over the Stiefel Manifold with Non-smooth Regularizers, Proceedings of the 2023 62nd IEEE Conference on Decision and Control, (2024). link
N. Xiao, X. Hu, X. Liu and K. Toh, CDOpt: A Python Package for a Class of Riemannian Optimization (under review). link
L. Wang and X. Liu, A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints, Journal of Industrial and Management Optimization, 19-10 (2023), 7753-7776. link
X. Hu, N. Xiao, X. Liu and K. Toh, An Improved Unconstrained Approach for Bilevel Optimization, SIAM Journal on Optimization, 33-4 (2023), 2801-2829. link
Y. Hu and X. Liu, The Exactness of the L1 Penalty Function for a Class of Mathematical Programs with Generalized Complementarity Constraints, Fundamental Research, 4-6 (2024), 1459-1464. link
X. Hu, N. Xiao, X. Liu and K. Toh, A Constraint Dissolving Approach for Nonsmooth Optimization over the Stiefel Manifold, IMA Journal of Numerical Analysis (2024). link
Y. Hu, X. Gao, Y. Zhao, X. Liu and H. Song, A Force-based Gradient Descent Method for Ab initio Atomic Structure Relaxation, Physical Review B, 106(2022), 104101. link
W. Liu, X. Liu and X. Chen, An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity, Journal of Machine Learning Research, 24.212 (2023), 1-43. link
Y. Hu and X. Liu, The Convergence Properties of Infeasible Inexact Proximal Alternating Linearized Minimization, SCIENCE CHINA Mathematics, 66-10 (2023), 2385-2410. link
S. Zhou, X. Liu and L. Xu, Stochastic Gauss-Newton Algorithms for Online PCA, Journal of Scientific Computing, 96-3 (2023), 72. link
N. Xiao, X. Liu and K. Toh, Dissolving Constraints for Riemannian Optimization, Mathematics of Operations Research, 49-1 (2023), 366-397. link
L. Wang and X. Liu, Decentralized Optimization Over the Stiefel Manifold by an Approximate Augmented Lagrangian Function, IEEE Transactions on Signal Processing, 70 (2022), 3029-3041. link
Y. Xu, X. Liu, X. Cao, et al., Artificial Intelligence: A Powerful Paradigm for Scientific Research, The Innovation, 2-4(2021), 100179. link
Y. Hu, H. Chen and X. Liu, A Global Optimization Approach for Multi-Marginal Optimal Transport Problems with Coulomb Cost, SIAM Journal on Scientific Computing, 45-3 (2023), A1214-A1238. link
N. Xiao and X. Liu, Solving Optimization Problems over the Stiefel Manifold by Smooth Exact Penalty Function, Journal of Computational Mathematics, 42-5 (2024), 1246-1276. link
W. Liu, X. Liu and X. Chen, Linearly-constrained Nonsmooth Optimization for Training Autoencoders, SIAM Journal on Optimization, 32-3 (2022), 1931-1957. link
X. Liu, N. Xiao and Y. Yuan, A Penalty-free Infeasible Approach for a Class of Nonsmooth Opimtization Problems over the Stiefel Manifold, Journal of Scientific Computing, 30 (2024), 99. link
L. Wang, X. Liu and Y. Zhang, A Communication-Efficient And Privacy-Aware Distributed Algorithm for Sparse PCA, Computational Optimization and Applications, 85 (2023), 1033–1072. link
L. Wang, X. Liu and Y. Zhang, Seeking Consensus on Subspaces in Federated Principal Component Analysis, Journal of Optimization Theory and Applications, DOI:10.1007/s10957-024-02523-1 (2024). link
L. Wang, B. Gao and X. Liu, Multipliers Correction Methods for Optimization Problems Over Stiefel Manifold,
CSIAM Transactions on Applied Mathematics, 2 (2021), 508-531. link
X. Liu, Z. Wen and Y. Yuan, Subspace Methods for Nonlinear Optimization, CSIAM Transactions on Applied Mathematics, 2 (2021), 585-651. link
B. Gao, G. Hu, Y. Kuang and X. Liu, An Orthogonalization-free Parallelizable Framework for All-electron Calculations in Density Funcitonal Theory, SIAM Journal on Scientific Computing, 44-3 (2022), B723-B745. link
N. Xiao, X. Liu and Y. Yuan, Exact Penalty Function for L21 Norm Minimization over the Stiefel Manifold, SIAM Journal on Optimization, 31-4 (2021), 3097-3126. link
N. Xiao, X. Liu and Y. Yuan, A Class of Smooth Exact Penalty Function Methods for Optimization Problems with Orthogonality Constraints, Optimization Methods and Software, DOI:10.1080/10556788.2020.1852236, 2020. link
Q. Dong, X. Yao, X. Liu, B. Liu and G. Zhai, Pseudo Complementary Measurement for the Traditional Single-pixel Camera, Chinese Physics B, 29-11 (2020), 114202. link
J. Hu, X. Liu, Z. Wen and Y. Yuan, A Brief Introduction to Manifold Optimization, Journal of the Operations Research Society of China, 8 (2020), 199-248. link
L. Wu, X. Liu and Z. Wen, Best Symmetric Rank-1 Approximation of Symmetric High-order Tensors, Optimization Methods and Software, 35-2 (2020), 416-438. link
Y. Shen and X. Liu, An Alternating Minimization Method for Matrix Completion Problems, Discrete and Continuous Dynamical Systems, 13-6 (2020), 1757-1772. link
B. Gao, X. Liu and Y. Yuan, Parallelizable Algorithms for Optimization Problems with Orthogonality Constraints, SIAM Journal on Scientific Computing, 41-3(2019), A1949–A1983. link
Y. Shen, H. Xu and X. Liu, An Alternating Minimization Method for Robust Principal Component Analysis, Optimization Methods and Software, 34-6 (2019), 1251-1276. link
C. Ma, X. Liu and Z. Wen, Globally Convergent Levenberg-Marquardt Method for Phase Retrieval, IEEE Transactions on Information Theory, 65-4 (2019), 1557-9654. link
Y. Liu, X. Liu and S. Ma, On the Nonergodic Convergence Rate of an Inexact Augmented Lagrangian Framework for Composite Convex Programming, Mathematics of Operations Research, 44-2(2019), 632-650. link
C. Chen, M. Li, X. Liu and Y. Ye, Extended ADMM and BCD for Nonseparable Convex Minimization Models with Quadratic Coupling Terms: Convergence Analysis and Insights, Mathematical Programming, 173(2019), 37-77. link
X. Liu, M. Ng, R. Zhang and Z. Zhang, A Continuous Optimization Model for Clustering, Mathematic Numerica Sinica (in Chinese), 40-4 (2018), 354-366. link
B. Gao, X. Liu, X. Chen and Y. Yuan, A New First-order Algorithmic Framework for Optimization Problems with Orthogonality Constraints, SIAM Journal on Optimization, 28-1(2018), 302–332. link
H. Wang, X. Liu, X. Chen and Y. Yuan, SNIG Property of Matrix Low-rank Factorization Model, Journal of Computational Mathematics, 36-3 (2018), 374-390. link
B. Gao, X. Liu and Y. Yuan, Algorithms for Optimization Problems with Orthogonality Constraints, OR Transactions (in Chinese), 21-4 (2017), 57-68. link
J. Hu, B. Jiang, X. Liu and Z. Wen, A Note on Semidefinite Programming Relaxations for Polynomial Optimization Over a Single Sphere, SCIENCE CHINA Mathematics, 59-8 (2016), 1543-1560. link
Z. Wen, C. Yang, X. Liu and Y. Zhang, Trace-Penalty Minimization for Large-scale Eigenspace Computation, Journal of Scientific Computing, 66-3 (2016), 1175-1203. link
X. Liu, Z. Wen and Y. Zhang, An Efficient Gauss-Newton Algorithm for Symmetric Low-Rank Product Matrix Approximations, SIAM Journal on Optimization, 25-3 (2015), 1571–1608. link
X. Liu, Z. Wen, X. Wang, M. Ulbrich and Y. Yuan, On the Analysis of the Discretized Kohn-Sham Density Functional Theory, SIAM Journal on Numerical Analysis, 53-4 (2015), 1758–1785. link
Q. Dong, X. Liu, Z. Wen and Y. Yuan, A Parallel Line Search Subspace Correction Method for Convex Optimization Problems, Journal of the Operations Research Society of China, 3 (2015), 163-187. link
X. Liu, X. Wang, Z. Wen and Y. Yuan, On the Convergence of the Self-Consistent Field Iteration in Kohn-Sham Density Functional Theory, SIAM Journal on Matrix Analysis and Applications, 35-2 (2014), 546-558. link
X. Liu, Z. Wen and Y. Zhang, Limited Memory Block Krylov Subspace Optimization for Computing Dominant Singular Value Decompositions, SIAM Journal on Scientific Computing, 35-3 (2013), A1641-A1668. link
X. Liu, C. Hao and M. Cheng, A Sequential Subspace Project Method for Linear Eigenvalue Problem, Asia Pacific Journal of Operational Research, 30-3 (2013). link
Z. Wen, C. Yang, X. Liu and S. Machesini, Alternating Direction Methods for Classical and Ptychographic Phase Retrieval, Inverse Problems, 28-11 (2012). link
Z. Wen, W. Yin, X. Liu and Y. Zhang, Introduction to Compressive Sensing and Sparse Optimization (in Chinese), OR Transactions, 16-3 (2012), 49-64. link
X. Liu, S. McKeeb, J. Yuan and Y. Yuan, Uniform Bounds on the 1-norm of the Inverse of Lower Triangular Toeplitz Matrices, Linear Algebra and its Applications, 435 (2011), 1157–1170. link
X. Liu, Numerical Methods for Special Nonlinear Least Squares Problems and L1 Norm Minimization Problems (in Chinese), Ph.D. thesis, AMSS, CAS (2009). pdf
X. Liu, Global Minimization of Quadratic Least-Squares Problems, Global-Link Informatics Limited, Hong Kong, Proceedings of the Ninth National Conference of Operation Research Society of China (2008), 188-193.
X. Liu and Y. Yuan, On the Separable Nonlinear Least Squares Problems, Journal of Computational Mathematics, 26 (2008), 390-403. link
X. Liu, An Efficient Unseparated Scheme for Separable Nonlinear Least Squares Problem, Global-Link Informatics Limited, Hong Kong, Proceedings of the Eighth National Conference of Operation Research Society of China (2006), 132-137.
X. Liu, Optimization Methods for Large-scale Sylvester Equation and the Separation of two Matrices, B.Sc. thesis, SMS, PKU (2004). pdf
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