Toeplitz-Plus-Hankel Matrix Recovery for Green's Function Computations on General Substrates. - Richard Y. Zhang; Jacob K. White 001 (Proc. IEEE, 2015)

Modified interior-point method for large-and-sparse low-rank semidefinite programs. - Richard Y. Zhang; Javad Lavaei (CDC, 2017)

Robust stability analysis for large-scale power systems. - Richard Yi Zhang (2017)

Conic Optimization Theory: Convexification Techniques and Numerical Algorithms. - Richard Y. Zhang; Cédric Josz; Somayeh Sojoudi (ACC, 2018)

Conic Approximation with Provable Guarantee for Traffic Signal Offset Optimization. - Yi Ouyang 002; Richard Y. Zhang; Javad Lavaei; Pravin Varaiya (CDC, 2018)

Sparse Semidefinite Programs with Near-Linear Time Complexity. - Richard Y. Zhang; Javad Lavaei (CDC, 2018)

Efficient Algorithm for Large-and-Sparse LMI Feasibility Problems. - Richard Y. Zhang; Javad Lavaei (CDC, 2018)

Sparse Inverse Covariance Estimation for Chordal Structures. - Salar Fattahi; Richard Y. Zhang; Somayeh Sojoudi (ECC, 2018)

Spurious Critical Points in Power System State Estimation. - Richard Y. Zhang; Javad Lavaei; Ross Baldick (HICSS, 2018)

Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion. - Richard Y. Zhang; Salar Fattahi; Somayeh Sojoudi (ICML, 2018)

A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization. - Cédric Josz; Yi Ouyang 002; Richard Y. Zhang; Javad Lavaei; Somayeh Sojoudi (NeurIPS, 2018)

How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? - Richard Y. Zhang; Cédric Josz; Somayeh Sojoudi; Javad Lavaei (NeurIPS, 2018)

GMRES-Accelerated ADMM for Quadratic Objectives. - Richard Y. Zhang; Jacob K. White 001 (SIAM J. Optim., 2018)

Monotonicity Between Phase Angles and Power Flow and Its Implications for the Uniqueness of Solutions. - Sangwoo Park 004; Richard Y. Zhang; Javad Lavaei; Ross Baldick (HICSS, 2019)

Linear-Time Algorithm for Learning Large-Scale Sparse Graphical Models. - Salar Fattahi; Richard Y. Zhang; Somayeh Sojoudi (IEEE Access, 2019)

Conic optimization for control, energy systems, and machine learning: Applications and algorithms. - Richard Y. Zhang; Cédric Josz; Somayeh Sojoudi (Annu. Rev. Control., 2019)

Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery. - Richard Y. Zhang; Somayeh Sojoudi; Javad Lavaei (J. Mach. Learn. Res., 2019)

Spurious Local Minima in Power System State Estimation. - Richard Y. Zhang; Javad Lavaei; Ross Baldick (IEEE Trans. Control. Netw. Syst., 2019)

On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples. - Richard Y. Zhang (NeurIPS, 2020)

How many samples is a good initial point worth in Low-rank Matrix Recovery? - Jialun Zhang; Richard Y. Zhang (NeurIPS, 2020)

How Many Samples is a Good Initial Point Worth? - Gavin Zhang; Richard Y. Zhang (CoRR, 2020)

Large-Scale Traffic Signal Offset Optimization. - Yi Ouyang 002; Richard Y. Zhang; Javad Lavaei; Pravin Varaiya (IEEE Trans. Control. Netw. Syst., 2020)

Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization. - Jialun Zhang; Salar Fattahi; Richard Y. Zhang (NeurIPS, 2021)

Sharp Global Guarantees for Nonconvex Low-Rank Matrix Recovery in the Overparameterized Regime. - Richard Y. Zhang (CoRR, 2021)

Sparse semidefinite programs with guaranteed near-linear time complexity via dualized clique tree conversion. - Richard Y. Zhang; Javad Lavaei (Math. Program., 2021)

Uniqueness of Power Flow Solutions Using Monotonicity and Network Topology. - SangWoo Park 004; Richard Y. Zhang; Javad Lavaei; Ross Baldick (IEEE Trans. Control. Netw. Syst., 2021)

Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion. - Jialun Zhang; Hong-Ming Chiu; Richard Y. Zhang (NeurIPS, 2022)

Improved Global Guarantees for the Nonconvex Burer-Monteiro Factorization via Rank Overparameterization. - Richard Y. Zhang (CoRR, 2022)

Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion. - Gavin Zhang; Hong-Ming Chiu; Richard Y. Zhang (CoRR, 2022)

Overcoming the Convex Relaxation Barrier for Neural Network Verification via Nonconvex Low-Rank Semidefinite Relaxations. - Hong-Ming Chiu; Richard Y. Zhang (CoRR, 2022)

Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations. - Hong-Ming Chiu; Richard Y. Zhang (ICML, 2023)

Fast and Minimax Optimal Estimation of Low-Rank Matrices via Non-Convex Gradient Descent. - Gavin Zhang; Hong-Ming Chiu; Richard Y. Zhang (CoRR, 2023)

Preconditioned Gradient Descent for Overparameterized Nonconvex Burer-Monteiro Factorization with Global Optimality Certification. - Gavin Zhang; Salar Fattahi; Richard Y. Zhang (J. Mach. Learn. Res., 2023)

Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent. - Jialun Zhang; Richard Y. Zhang; Hong-Ming Chiu (AISTATS, 2024)

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming. - Yubo Zhuang; Xiaohui Chen; Yun Yang; Richard Y. Zhang (ICLR, 2024)

Inferring Cell Differentiation Maps from Lineage Tracing Data. - Palash Sashittal; Richard Y. Zhang; Benjamin K. Law; Henri Schmidt; Alexander Strzalkowski; Adriano Bolondi; Michelle M. Chan; Benjamin J. Raphael (RECOMB, 2025)

Simple Alternating Minimization Provably Solves Complete Dictionary Learning. - Geyu Liang; Gavin Zhang; Salar Fattahi; Richard Y. Zhang (SIAM J. Math. Data Sci., 2025)

Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization. - Gavin Zhang; Salar Fattahi; Richard Y. Zhang (CoRR, 2025)

Nonnegative Low-rank Matrix Recovery Can Have Spurious Local Minima. - Richard Y. Zhang (CoRR, 2025)

SDP-CROWN: Efficient Bound Propagation for Neural Network Verification with Tightness of Semidefinite Programming. - Hong-Ming Chiu; Hao Chen; Huan Zhang; Richard Y. Zhang (CoRR, 2025)