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2020 – today
- 2024
[j52]Guanghui Lan, Alexander Shapiro:
Numerical Methods for Convex Multistage Stochastic Optimization. Found. Trends Optim. 6(2): 63-144 (2024)- [j51]Yan Li
, Guanghui Lan, Tuo Zhao:
Homotopic policy mirror descent: policy convergence, algorithmic regularization, and improved sample complexity. Math. Program. 207(1): 457-513 (2024) - [j50]Tianjiao Li, Ziwei Guan, Shaofeng Zou, Tengyu Xu, Yingbin Liang, Guanghui Lan:
Faster algorithm and sharper analysis for constrained Markov decision process. Oper. Res. Lett. 54: 107107 (2024) - [i43]Caleb Ju, Guanghui Lan:
Strongly-Polynomial Time and Validation Analysis of Policy Gradient Methods. CoRR abs/2409.19437 (2024) - 2023
- [j49]Digvijay Boob, Qi Deng, Guanghui Lan:
Stochastic first-order methods for convex and nonconvex functional constrained optimization. Math. Program. 197(1): 215-279 (2023) - [j48]Guanghui Lan:
Policy mirror descent for reinforcement learning: linear convergence, new sampling complexity, and generalized problem classes. Math. Program. 198(1): 1059-1106 (2023) - [j47]Zi Xu, Huiling Zhang, Yang Xu, Guanghui Lan:
A unified single-loop alternating gradient projection algorithm for nonconvex-concave and convex-nonconcave minimax problems. Math. Program. 201(1): 635-706 (2023) - [j46]Guanghui Lan, Zhe Zhang:
Optimal Methods for Convex Risk-Averse Distributed Optimization. SIAM J. Optim. 33(3): 1518-1557 (2023) - [j45]Guanghui Lan, Yuyuan Ouyang, Yi Zhou:
Graph Topology Invariant Gradient and Sampling Complexity for Decentralized and Stochastic Optimization. SIAM J. Optim. 33(3): 1647-1675 (2023) - [j44]Guanghui Lan, Yan Li, Tuo Zhao:
Block Policy Mirror Descent. SIAM J. Optim. 33(3): 2341-2378 (2023) - [j43]Tianjiao Li, Guanghui Lan, Ashwin Pananjady:
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation. SIAM J. Math. Data Sci. 5(1): 174-200 (2023) - [i42]Yan Li, Guanghui Lan:
Policy Mirror Descent Inherently Explores Action Space. CoRR abs/2303.04386 (2023) - [i41]Guanghui Lan, Alexander Shapiro:
Numerical Methods for Convex Multistage Stochastic Optimization. CoRR abs/2303.15672 (2023) - [i40]Sasila Ilandarideva, Anatoli B. Juditsky, Guanghui Lan, Tianjiao Li:
Accelerated stochastic approximation with state-dependent noise. CoRR abs/2307.01497 (2023) - [i39]Yan Li, Guanghui Lan:
First-order Policy Optimization for Robust Policy Evaluation. CoRR abs/2307.15890 (2023) - [i38]Tianjiao Li, Guanghui Lan:
A simple uniformly optimal method without line search for convex optimization. CoRR abs/2310.10082 (2023) - 2022
- [j42]Guanghui Lan, Yuyuan Ouyang:
Accelerated gradient sliding for structured convex optimization. Comput. Optim. Appl. 82(2): 361-394 (2022) - [j41]Digvijay Boob, Santanu S. Dey, Guanghui Lan:
Complexity of training ReLU neural network. Discret. Optim. 44(Part): 100620 (2022) - [j40]Guanghui Lan:
Complexity of stochastic dual dynamic programming. Math. Program. 191(2): 717-754 (2022) - [j39]Guanghui Lan:
Correction to: Complexity of stochastic dual dynamic programming. Math. Program. 194(1): 1187-1189 (2022) - [j38]Georgios Kotsalis, Guanghui Lan, Tianjiao Li:
Simple and Optimal Methods for Stochastic Variational Inequalities, II: Markovian Noise and Policy Evaluation in Reinforcement Learning. SIAM J. Optim. 32(2): 1120-1155 (2022) - [j37]Georgios Kotsalis, Guanghui Lan, Tianjiao Li:
Simple and Optimal Methods for Stochastic Variational Inequalities, I: Operator Extrapolation. SIAM J. Optim. 32(3): 2041-2073 (2022) - [c9]Yan Li, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam, Tuo Zhao, Guanghui Lan:
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits. ICLR 2022 - [i37]Guanghui Lan, Yan Li, Tuo Zhao:
Block Policy Mirror Descent. CoRR abs/2201.05756 (2022) - [i36]Yan Li, Tuo Zhao, Guanghui Lan:
Homotopic Policy Mirror Descent: Policy Convergence, Implicit Regularization, and Improved Sample Complexity. CoRR abs/2201.09457 (2022) - [i35]Shuoguang Yang, Xudong Li, Guanghui Lan:
Data-Driven Minimax Optimization with Expectation Constraints. CoRR abs/2202.07868 (2022) - [i34]Guanghui Lan, Zhe Zhang:
Optimal Methods for Risk Averse Distributed Optimization. CoRR abs/2203.05117 (2022) - [i33]Tianjiao Li, Feiyang Wu, Guanghui Lan:
Stochastic first-order methods for average-reward Markov decision processes. CoRR abs/2205.05800 (2022) - [i32]Yan Li, Tuo Zhao, Guanghui Lan:
First-order Policy Optimization for Robust Markov Decision Process. CoRR abs/2209.10579 (2022) - [i31]Yi Cheng, Guanghui Lan, H. Edwin Romeijn:
Functional Constrained Optimization for Risk Aversion and Sparsity Control. CoRR abs/2210.05108 (2022) - [i30]Guanghui Lan:
Policy Optimization over General State and Action Spaces. CoRR abs/2211.16715 (2022) - 2021
- [j36]Guanghui Lan, Yi Zhou:
Asynchronous Decentralized Accelerated Stochastic Gradient Descent. IEEE J. Sel. Areas Inf. Theory 2(2): 802-811 (2021) - [j35]Guanghui Lan, Zhiqiang Zhou:
Dynamic stochastic approximation for multi-stage stochastic optimization. Math. Program. 187(1): 487-532 (2021) - [j34]Georgios Kotsalis, Guanghui Lan, Arkadi S. Nemirovsky:
Convex Optimization for Finite-Horizon Robust Covariance Control of Linear Stochastic Systems. SIAM J. Control. Optim. 59(1): 296-319 (2021) - [j33]Zhe Zhang, Shabbir Ahmed, Guanghui Lan:
Efficient Algorithms for Distributionally Robust Stochastic Optimization with Discrete Scenario Support. SIAM J. Optim. 31(3): 1690-1721 (2021) - [j32]Guanghui Lan, H. Edwin Romeijn, Zhiqiang Zhou:
Conditional Gradient Methods for Convex Optimization with General Affine and Nonlinear Constraints. SIAM J. Optim. 31(3): 2307-2339 (2021) - [c8]Tengyu Xu, Yingbin Liang, Guanghui Lan:
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee. ICML 2021: 11480-11491 - [i29]Guanghui Lan:
Policy Mirror Descent for Reinforcement Learning: Linear Convergence, New Sampling Complexity, and Generalized Problem Classes. CoRR abs/2102.00135 (2021) - [i28]Yan Li, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam, Tuo Zhao, Guanghui Lan:
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits. CoRR abs/2110.04844 (2021) - [i27]Tianjiao Li, Ziwei Guan, Shaofeng Zou, Tengyu Xu, Yingbin Liang, Guanghui Lan:
Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process. CoRR abs/2110.10351 (2021) - [i26]Tianjiao Li, Guanghui Lan, Ashwin Pananjady:
Accelerated and instance-optimal policy evaluation with linear function approximation. CoRR abs/2112.13109 (2021) - 2020
- [j31]Guanghui Lan, Zhiqiang Zhou:
Algorithms for stochastic optimization with function or expectation constraints. Comput. Optim. Appl. 76(2): 461-498 (2020) - [j30]Guanghui Lan, Soomin Lee, Yi Zhou:
Communication-efficient algorithms for decentralized and stochastic optimization. Math. Program. 180(1): 237-284 (2020) - [c7]Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, Le Song:
GLAD: Learning Sparse Graph Recovery. ICLR 2020 - [c6]Digvijay Boob, Qi Deng, Guanghui Lan, Yilin Wang:
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization. NeurIPS 2020 - [i25]Zi Xu, Huiling Zhang, Yang Xu, Guanghui Lan:
A Unified Single-loop Alternating Gradient Projection Algorithm for Nonconvex-Concave and Convex-Nonconcave Minimax Problems. CoRR abs/2006.02032 (2020) - [i24]Guanghui Lan, H. Edwin Romeijn, Zhiqiang Zhou:
Conditional Gradient Methods for Convex Optimization with Function Constraints. CoRR abs/2007.00153 (2020) - [i23]Digvijay Boob, Qi Deng, Guanghui Lan, Yilin Wang:
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization. CoRR abs/2010.12169 (2020) - [i22]Georgios Kotsalis, Guanghui Lan, Tianjiao Li:
Simple and optimal methods for stochastic variational inequalities, I: operator extrapolation. CoRR abs/2011.02987 (2020) - [i21]Tengyu Xu, Yingbin Liang, Guanghui Lan:
A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis. CoRR abs/2011.05869 (2020) - [i20]Georgios Kotsalis, Guanghui Lan, Tianjiao Li:
Simple and optimal methods for stochastic variational inequalities, II: Markovian noise and policy evaluation in reinforcement learning. CoRR abs/2011.08434 (2020) - [i19]Zhe Zhang, Guanghui Lan:
Optimal Algorithms for Convex Nested Stochastic Composite Optimization. CoRR abs/2011.10076 (2020)
2010 – 2019
- 2019
- [j29]Yunmei Chen, Guanghui Lan, Yuyuan Ouyang, Wei Zhang:
Fast bundle-level methods for unconstrained and ball-constrained convex optimization. Comput. Optim. Appl. 73(1): 159-199 (2019) - [j28]Saeed Ghadimi, Guanghui Lan, Hongchao Zhang:
Generalized Uniformly Optimal Methods for Nonlinear Programming. J. Sci. Comput. 79(3): 1854-1881 (2019) - [j27]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
A note on inexact gradient and Hessian conditions for cubic regularized Newton's method. Oper. Res. Lett. 47(2): 146-149 (2019) - [j26]Guanghui Lan, Yu Yang:
Accelerated Stochastic Algorithms for Nonconvex Finite-Sum and Multiblock Optimization. SIAM J. Optim. 29(4): 2753-2784 (2019) - [c5]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. AISTATS 2019: 2731-2740 - [c4]Guanghui Lan, Zhize Li, Yi Zhou:
A unified variance-reduced accelerated gradient method for convex optimization. NeurIPS 2019: 10462-10472 - [c3]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Cubic Regularization with Momentum for Nonconvex Optimization. UAI 2019: 313-322 - [i18]Guanghui Lan, Zhize Li, Yi Zhou:
A unified variance-reduced accelerated gradient method for convex optimization. CoRR abs/1905.12412 (2019) - [i17]Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Le Song:
GLAD: Learning Sparse Graph Recovery. CoRR abs/1906.00271 (2019) - [i16]Digvijay Boob, Qi Deng, Guanghui Lan:
Proximal Point Methods for Optimization with Nonconvex Functional Constraints. CoRR abs/1908.02734 (2019) - [i15]Guanghui Lan:
Complexity of Stochastic Dual Dynamic Programming. CoRR abs/1912.07702 (2019) - 2018
- [j25]Guanghui Lan, Yi Zhou:
An optimal randomized incremental gradient method. Math. Program. 171(1-2): 167-215 (2018) - [j24]Guanghui Lan, Yi Zhou:
Random Gradient Extrapolation for Distributed and Stochastic Optimization. SIAM J. Optim. 28(4): 2753-2782 (2018) - [i14]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. CoRR abs/1802.07372 (2018) - [i13]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
A Note on Inexact Condition for Cubic Regularized Newton's Method. CoRR abs/1808.07384 (2018) - [i12]Guanghui Lan, Yi Zhou:
Asynchronous decentralized accelerated stochastic gradient descent. CoRR abs/1809.09258 (2018) - [i11]Digvijay Boob, Santanu S. Dey, Guanghui Lan:
Complexity of Training ReLU Neural Network. CoRR abs/1809.10787 (2018) - [i10]Qi Deng, Yi Cheng, Guanghui Lan:
Optimal Adaptive and Accelerated Stochastic Gradient Descent. CoRR abs/1810.00553 (2018) - [i9]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Cubic Regularization with Momentum for Nonconvex Optimization. CoRR abs/1810.03763 (2018) - 2017
- [j23]Yunmei Chen, Guanghui Lan, Yuyuan Ouyang:
Accelerated schemes for a class of variational inequalities. Math. Program. 165(1): 113-149 (2017) - [c2]Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink:
Conditional Accelerated Lazy Stochastic Gradient Descent. ICML 2017: 1965-1974 - [i8]Guanghui Lan, Soomin Lee, Yi Zhou:
Communication-Efficient Algorithms for Decentralized and Stochastic Optimization. CoRR abs/1701.03961 (2017) - [i7]Guanghui Lan, Sebastian Pokutta, Yi Zhou, Daniel Zink:
Conditional Accelerated Lazy Stochastic Gradient Descent. CoRR abs/1703.05840 (2017) - [i6]Guanghui Lan, Zhiqiang Zhou:
Dynamic Stochastic Approximation for Multi-stage Stochastic Optimization. CoRR abs/1707.03324 (2017) - [i5]Digvijay Boob, Guanghui Lan:
Theoretical properties of the global optimizer of two layer neural network. CoRR abs/1710.11241 (2017) - [i4]Guanghui Lan, Yi Zhou:
Random gradient extrapolation for distributed and stochastic optimization. CoRR abs/1711.05762 (2017) - 2016
- [j22]Saeed Ghadimi, Guanghui Lan, Hongchao Zhang:
Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization. Math. Program. 155(1-2): 267-305 (2016) - [j21]Guanghui Lan, Renato D. C. Monteiro:
Iteration-complexity of first-order augmented Lagrangian methods for convex programming. Math. Program. 155(1-2): 511-547 (2016) - [j20]Saeed Ghadimi, Guanghui Lan:
Accelerated gradient methods for nonconvex nonlinear and stochastic programming. Math. Program. 156(1-2): 59-99 (2016) - [j19]Guanghui Lan:
Gradient sliding for composite optimization. Math. Program. 159(1-2): 201-235 (2016) - [j18]Guanghui Lan, Yi Zhou:
Conditional Gradient Sliding for Convex Optimization. SIAM J. Optim. 26(2): 1379-1409 (2016) - 2015
- [j17]Cong D. Dang, Guanghui Lan:
On the convergence properties of non-Euclidean extragradient methods for variational inequalities with generalized monotone operators. Comput. Optim. Appl. 60(2): 277-310 (2015) - [j16]Guanghui Lan:
Bundle-level type methods uniformly optimal for smooth and nonsmooth convex optimization. Math. Program. 149(1-2): 1-45 (2015) - [j15]Yuyuan Ouyang, Yunmei Chen, Guanghui Lan, Eduardo Pasiliao Jr.:
An Accelerated Linearized Alternating Direction Method of Multipliers. SIAM J. Imaging Sci. 8(1): 644-681 (2015) - [j14]Cong D. Dang, Guanghui Lan:
Stochastic Block Mirror Descent Methods for Nonsmooth and Stochastic Optimization. SIAM J. Optim. 25(2): 856-881 (2015) - [c1]Hao Zhang, Justin C. Park, Yunmei Chen, Guanghui Lan, Bo Lu:
A novel method for 4D cone-beam computer-tomography reconstruction. Medical Imaging: Image Processing 2015: 941324 - [i3]Guanghui Lan, Yi Zhou:
An optimal randomized incremental gradient method. CoRR abs/1507.02000 (2015) - 2014
- [j13]Cong D. Dang, Kaiyu Dai, Guanghui Lan:
A linearly convergent first-order algorithm for total variation minimisation in image processing. Int. J. Bioinform. Res. Appl. 10(1): 4-26 (2014) - [j12]Yunmei Chen, Guanghui Lan, Yuyuan Ouyang:
Optimal Primal-Dual Methods for a Class of Saddle Point Problems. SIAM J. Optim. 24(4): 1779-1814 (2014) - [i2]Guanghui Lan:
Gradient Sliding for Composite Optimization. CoRR abs/1406.0919 (2014) - 2013
- [j11]Guanghui Lan, Renato D. C. Monteiro:
Iteration-complexity of first-order penalty methods for convex programming. Math. Program. 138(1-2): 115-139 (2013) - [j10]Saeed Ghadimi, Guanghui Lan:
Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization, II: Shrinking Procedures and Optimal Algorithms. SIAM J. Optim. 23(4): 2061-2089 (2013) - [j9]Saeed Ghadimi, Guanghui Lan:
Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming. SIAM J. Optim. 23(4): 2341-2368 (2013) - [i1]Saeed Ghadimi, Guanghui Lan:
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming. CoRR abs/1309.5549 (2013) - 2012
- [j8]Guanghui Lan:
An optimal method for stochastic composite optimization. Math. Program. 133(1-2): 365-397 (2012) - [j7]Guanghui Lan, Arkadi Nemirovski, Alexander Shapiro:
Validation analysis of mirror descent stochastic approximation method. Math. Program. 134(2): 425-458 (2012) - [j6]Saeed Ghadimi, Guanghui Lan:
Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization I: A Generic Algorithmic Framework. SIAM J. Optim. 22(4): 1469-1492 (2012) - 2011
- [j5]Guanghui Lan, Zhaosong Lu, Renato D. C. Monteiro:
Primal-dual first-order methods with O(1/e) iteration-complexity for cone programming. Math. Program. 126(1): 1-29 (2011)
2000 – 2009
- 2009
- [j4]Arkadi Nemirovski, Anatoli B. Juditsky, Guanghui Lan, Alexander Shapiro:
Robust Stochastic Approximation Approach to Stochastic Programming. SIAM J. Optim. 19(4): 1574-1609 (2009) - [j3]Guanghui Lan, Renato D. C. Monteiro, Takashi Tsuchiya:
A Polynomial Predictor-Corrector Trust-Region Algorithm for Linear Programming. SIAM J. Optim. 19(4): 1918-1946 (2009) - 2007
- [j2]Guanghui Lan, Gail W. DePuy, Gary E. Whitehouse:
An effective and simple heuristic for the set covering problem. Eur. J. Oper. Res. 176(3): 1387-1403 (2007) - 2006
- [j1]Guanghui Lan, Gail W. DePuy:
On the effectiveness of incorporating randomness and memory into a multi-start metaheuristic with application to the Set Covering Problem. Comput. Ind. Eng. 51(3): 362-374 (2006)
Coauthor Index
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