On the effectiveness of incorporating randomness and memory into a multi-start metaheuristic with application to the Set Covering Problem. - Guanghui Lan; Gail W. DePuy (Comput. Ind. Eng., 2006)

An effective and simple heuristic for the set covering problem. - Guanghui Lan; Gail W. DePuy; Gary E. Whitehouse (Eur. J. Oper. Res., 2007)

A Polynomial Predictor-Corrector Trust-Region Algorithm for Linear Programming. - Guanghui Lan; Renato D. C. Monteiro; Takashi Tsuchiya (SIAM J. Optim., 2009)

Robust Stochastic Approximation Approach to Stochastic Programming. - Arkadi Nemirovski; Anatoli B. Juditsky; Guanghui Lan; Alexander Shapiro 001 (SIAM J. Optim., 2009)

Convex optimization under inexact first-order information. - Guanghui Lan (2009)

Primal-dual first-order methods with O(1/e) iteration-complexity for cone programming. - Guanghui Lan; Zhaosong Lu; Renato D. C. Monteiro (Math. Program., 2011)

An optimal method for stochastic composite optimization. - Guanghui Lan (Math. Program., 2012)

Validation analysis of mirror descent stochastic approximation method. - Guanghui Lan; Arkadi Nemirovski; Alexander Shapiro 001 (Math. Program., 2012)

Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization I: A Generic Algorithmic Framework. - Saeed Ghadimi; Guanghui Lan (SIAM J. Optim., 2012)

Iteration-complexity of first-order penalty methods for convex programming. - Guanghui Lan; Renato D. C. Monteiro (Math. Program., 2013)

Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization, II: Shrinking Procedures and Optimal Algorithms. - Saeed Ghadimi; Guanghui Lan (SIAM J. Optim., 2013)

Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming. - Saeed Ghadimi; Guanghui Lan (SIAM J. Optim., 2013)

A linearly convergent first-order algorithm for total variation minimisation in image processing. - Cong D. Dang; Kaiyu Dai; Guanghui Lan (Int. J. Bioinform. Res. Appl., 2014)

Optimal Primal-Dual Methods for a Class of Saddle Point Problems. - Yunmei Chen; Guanghui Lan; Yuyuan Ouyang (SIAM J. Optim., 2014)

A novel method for 4D cone-beam computer-tomography reconstruction. - Hao Zhang 030; Justin C. Park; Yunmei Chen; Guanghui Lan; Bo Lu (Image Processing, 2015)

On the convergence properties of non-Euclidean extragradient methods for variational inequalities with generalized monotone operators. - Cong D. Dang; Guanghui Lan (Comput. Optim. Appl., 2015)

Bundle-level type methods uniformly optimal for smooth and nonsmooth convex optimization. - Guanghui Lan (Math. Program., 2015)

An Accelerated Linearized Alternating Direction Method of Multipliers. - Yuyuan Ouyang; Yunmei Chen; Guanghui Lan; Eduardo Pasiliao Jr. (SIAM J. Imaging Sci., 2015)

Stochastic Block Mirror Descent Methods for Nonsmooth and Stochastic Optimization. - Cong D. Dang; Guanghui Lan (SIAM J. Optim., 2015)

Accelerated gradient methods for nonconvex nonlinear and stochastic programming. - Saeed Ghadimi; Guanghui Lan (Math. Program., 2016)

Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization. - Saeed Ghadimi; Guanghui Lan; Hongchao Zhang (Math. Program., 2016)

Gradient sliding for composite optimization. - Guanghui Lan (Math. Program., 2016)

Iteration-complexity of first-order augmented Lagrangian methods for convex programming. - Guanghui Lan; Renato D. C. Monteiro (Math. Program., 2016)

Conditional Gradient Sliding for Convex Optimization. - Guanghui Lan; Yi Zhou 015 (SIAM J. Optim., 2016)

Conditional Accelerated Lazy Stochastic Gradient Descent. - Guanghui Lan; Sebastian Pokutta; Yi Zhou 015; Daniel Zink (ICML, 2017)

Theoretical properties of the global optimizer of two layer neural network. - Digvijay Boob; Guanghui Lan (CoRR, 2017)

Accelerated schemes for a class of variational inequalities. - Yunmei Chen; Guanghui Lan; Yuyuan Ouyang (Math. Program., 2017)

Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. - Zhe Wang 021; Yi Zhou 017; Yingbin Liang; Guanghui Lan (CoRR, 2018)

A Note on Inexact Condition for Cubic Regularized Newton's Method. - Zhe Wang 021; Yi Zhou 017; Yingbin Liang; Guanghui Lan (CoRR, 2018)

Optimal Adaptive and Accelerated Stochastic Gradient Descent. - Qi Deng; Yi Cheng; Guanghui Lan (CoRR, 2018)

An optimal randomized incremental gradient method. - Guanghui Lan; Yi Zhou 015 (Math. Program., 2018)

Random Gradient Extrapolation for Distributed and Stochastic Optimization. - Guanghui Lan; Yi Zhou 015 (SIAM J. Optim., 2018)

Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. - Zhe Wang 021; Yi Zhou 017; Yingbin Liang; Guanghui Lan (AISTATS, 2019)

A unified variance-reduced accelerated gradient method for convex optimization. - Guanghui Lan; Zhize Li; Yi Zhou 015 (NeurIPS, 2019)

Cubic Regularization with Momentum for Nonconvex Optimization. - Zhe Wang 021; Yi Zhou 017; Yingbin Liang; Guanghui Lan (UAI, 2019)

Fast bundle-level methods for unconstrained and ball-constrained convex optimization. - Yunmei Chen; Guanghui Lan; Yuyuan Ouyang; Wei Zhang 216 (Comput. Optim. Appl., 2019)

GLAD: Learning Sparse Graph Recovery. - Harsh Shrivastava 001; Xinshi Chen; Binghong Chen; Guanghui Lan; Srinivas Aluru; Le Song (CoRR, 2019)

Proximal Point Methods for Optimization with Nonconvex Functional Constraints. - Digvijay Boob; Qi Deng; Guanghui Lan (CoRR, 2019)

Generalized Uniformly Optimal Methods for Nonlinear Programming. - Saeed Ghadimi; Guanghui Lan; Hongchao Zhang (J. Sci. Comput., 2019)

A note on inexact gradient and Hessian conditions for cubic regularized Newton's method. - Zhe Wang 021; Yi Zhou 017; Yingbin Liang; Guanghui Lan (Oper. Res. Lett., 2019)

Accelerated Stochastic Algorithms for Nonconvex Finite-Sum and Multiblock Optimization. - Guanghui Lan; Yu Yang 014 (SIAM J. Optim., 2019)

GLAD: Learning Sparse Graph Recovery. - Harsh Shrivastava 001; Xinshi Chen; Binghong Chen; Guanghui Lan; Srinivas Aluru; Han Liu 001; Le Song (ICLR, 2020)

A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization. - Digvijay Boob; Qi Deng; Guanghui Lan; Yilin Wang (NeurIPS, 2020)

Algorithms for stochastic optimization with function or expectation constraints. - Guanghui Lan; Zhiqiang Zhou (Comput. Optim. Appl., 2020)

Conditional Gradient Methods for Convex Optimization with Function Constraints. - Guanghui Lan; H. Edwin Romeijn; Zhiqiang Zhou (CoRR, 2020)

A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis. - Tengyu Xu; Yingbin Liang; Guanghui Lan (CoRR, 2020)

Optimal Algorithms for Convex Nested Stochastic Composite Optimization. - Zhe Zhang; Guanghui Lan (CoRR, 2020)

Communication-efficient algorithms for decentralized and stochastic optimization. - Guanghui Lan; Soomin Lee; Yi Zhou 015 (Math. Program., 2020)

CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee. - Tengyu Xu; Yingbin Liang; Guanghui Lan (ICML, 2021)

Asynchronous Decentralized Accelerated Stochastic Gradient Descent. - Guanghui Lan; Yi Zhou 015 (IEEE J. Sel. Areas Inf. Theory, 2021)

Dynamic stochastic approximation for multi-stage stochastic optimization. - Guanghui Lan; Zhiqiang Zhou (Math. Program., 2021)

Convex Optimization for Finite-Horizon Robust Covariance Control of Linear Stochastic Systems. - Georgios Kotsalis; Guanghui Lan; Arkadi S. Nemirovsky (SIAM J. Control. Optim., 2021)

Conditional Gradient Methods for Convex Optimization with General Affine and Nonlinear Constraints. - Guanghui Lan; H. Edwin Romeijn; Zhiqiang Zhou (SIAM J. Optim., 2021)

Efficient Algorithms for Distributionally Robust Stochastic Optimization with Discrete Scenario Support. - Zhe Zhang; Shabbir Ahmed; Guanghui Lan (SIAM J. Optim., 2021)

Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits. - Yan Li 074; Dhruv Choudhary; Xiaohan Wei; Baichuan Yuan; Bhargav Bhushanam; Tuo Zhao; Guanghui Lan (ICLR, 2022)

Accelerated gradient sliding for structured convex optimization. - Guanghui Lan; Yuyuan Ouyang (Comput. Optim. Appl., 2022)

Homotopic Policy Mirror Descent: Policy Convergence, Implicit Regularization, and Improved Sample Complexity. - Yan Li 074; Tuo Zhao; Guanghui Lan (CoRR, 2022)

Data-Driven Minimax Optimization with Expectation Constraints. - Shuoguang Yang; Xudong Li; Guanghui Lan (CoRR, 2022)

Optimal Methods for Risk Averse Distributed Optimization. - Guanghui Lan; Zhe Zhang (CoRR, 2022)

Stochastic first-order methods for average-reward Markov decision processes. - Tianjiao Li; Feiyang Wu; Guanghui Lan (CoRR, 2022)

First-order Policy Optimization for Robust Markov Decision Process. - Yan Li 074; Tuo Zhao; Guanghui Lan (CoRR, 2022)

Functional Constrained Optimization for Risk Aversion and Sparsity Control. - Yi Cheng; Guanghui Lan; H. Edwin Romeijn (CoRR, 2022)

Policy Optimization over General State and Action Spaces. - Guanghui Lan (CoRR, 2022)

Complexity of training ReLU neural network. - Digvijay Boob; Santanu S. Dey; Guanghui Lan (Discret. Optim., 2022)

Complexity of stochastic dual dynamic programming. - Guanghui Lan (Math. Program., 2022)

Correction to: Complexity of stochastic dual dynamic programming. - Guanghui Lan (Math. Program., 2022)

Simple and Optimal Methods for Stochastic Variational Inequalities, II: Markovian Noise and Policy Evaluation in Reinforcement Learning. - Georgios Kotsalis; Guanghui Lan; Tianjiao Li (SIAM J. Optim., 2022)

Simple and Optimal Methods for Stochastic Variational Inequalities, I: Operator Extrapolation. - Georgios Kotsalis; Guanghui Lan; Tianjiao Li (SIAM J. Optim., 2022)

First-order Policy Optimization for Robust Policy Evaluation. - Yan Li 074; Guanghui Lan (CoRR, 2023)

A simple uniformly optimal method without line search for convex optimization. - Tianjiao Li; Guanghui Lan (CoRR, 2023)

A unified single-loop alternating gradient projection algorithm for nonconvex-concave and convex-nonconcave minimax problems. - Zi Xu; Huiling Zhang; Yang Xu; Guanghui Lan (Math. Program., 2023)

Stochastic first-order methods for convex and nonconvex functional constrained optimization. - Digvijay Boob; Qi Deng; Guanghui Lan (Math. Program., 2023)

Policy mirror descent for reinforcement learning: linear convergence, new sampling complexity, and generalized problem classes. - Guanghui Lan (Math. Program., 2023)

Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation. - Tianjiao Li; Guanghui Lan; Ashwin Pananjady (SIAM J. Math. Data Sci., 2023)

Block Policy Mirror Descent. - Guanghui Lan; Yan Li 074; Tuo Zhao (SIAM J. Optim., 2023)

Graph Topology Invariant Gradient and Sampling Complexity for Decentralized and Stochastic Optimization. - Guanghui Lan; Yuyuan Ouyang; Yi Zhou 015 (SIAM J. Optim., 2023)

Optimal Methods for Convex Risk-Averse Distributed Optimization. - Guanghui Lan; Zhe Zhang (SIAM J. Optim., 2023)

Strongly-Polynomial Time and Validation Analysis of Policy Gradient Methods. - Caleb Ju; Guanghui Lan (CoRR, 2024)

Auto-conditioned primal-dual hybrid gradient method and alternating direction method of multipliers. - Guanghui Lan; Tianjiao Li (CoRR, 2024)

Projected gradient methods for nonconvex and stochastic optimization: new complexities and auto-conditioned stepsizes. - Guanghui Lan; Tianjiao Li; Yangyang Xu (CoRR, 2024)

Numerical Methods for Convex Multistage Stochastic Optimization. - Guanghui Lan; Alexander Shapiro 001 (Found. Trends Optim., 2024)

Faster algorithm and sharper analysis for constrained Markov decision process. - Tianjiao Li; Ziwei Guan; Shaofeng Zou; Tengyu Xu; Yingbin Liang; Guanghui Lan (Oper. Res. Lett., 2024)

Homotopic policy mirror descent: policy convergence, algorithmic regularization, and improved sample complexity. - Yan Li 074; Guanghui Lan; Tuo Zhao (Math. Program., 2024)

Data-Driven Minimax Optimization with Expectation Constraints. - Shuoguang Yang; Xudong Li 004; Guanghui Lan (Oper. Res., 2025)

A Model-Free First-Order Method for Linear Quadratic Regulator with ( boldsymbol{ tilde {O}(1/ varepsilon )} ) Sampling Complexity. - Caleb Ju; Georgios Kotsalis; Guanghui Lan (SIAM J. Control. Optim., 2025)

Policy Mirror Descent Inherently Explores Action Space. - Yan Li 074; Guanghui Lan (SIAM J. Optim., 2025)

Level constrained first order methods for function constrained optimization. - Digvijay Boob; Qi Deng; Guanghui Lan (Math. Program., 2025)

Optimal methods for convex nested stochastic composite optimization. - Zhe Zhang; Guanghui Lan (Math. Program., 2025)

Accelerated stochastic approximation with state-dependent noise. - Sasila Ilandarideva; Anatoli B. Juditsky; Guanghui Lan; Tianjiao Li (Math. Program., 2025)