Yu-Hong Dai
Basic Information
Education and Experiences
Selected Awards and Talks
Researches
Publications
Software and Applications
Students and Postdocs
Services
Responsibility and Editorship
Organized Activities
Special Pictures

Publications

  1. Y.H. Dai and Y. Yuan (1996), Convergence Properties of the Fletcher-Reeves Method, IMA Journal of Numerical Analysis, 16:2, pp. 155-164.

  2. Y.H. Dai and Y. Yuan (1996), Convergence Properties of the Conjugate Descent Method, Advances in Mathematics, 6, pp. 552-562.

  3. Y.H. Dai and Y. Yuan (1996), Convergence of the Fletcher-Reeves Method under A Generalized Wolfe Search, Numer. Math. J. Chinese Univ., 2, pp. 142-148.

  4. Y.H. Dai and Y. Yuan (1998), Convergence Properties of the Beale-Powell Restart Algorithm, Science in China (series A), 41:11, pp. 1142-1150.

  5. Y.H. Dai and Y. Yuan (1998), Some Properties of A New Conjugate Gradient Method, in: Y. Yuan ed., Advances in Nonlinear Programming (Kluwer, Boston), pp. 251-262.

  6. Y.H. Dai, J. Y. Han, G. H. Liu, D. F. Sun, H. X. Yin, and Y. Yuan (1999), Convergence Properties of Nonlinear Conjugate Gradient Methods, SIAM Journal on Optimization, 10:2, pp. 345-358.

  7. Y.H. Dai and Y. Yuan (1999), A Nonlinear Conjugate Gradient Method with A Strong Global Convergence Property, SIAM Journal on Optimization, 10:1, pp. 177-182.

  8. Y.H. Dai and Y. Yuan (1999), Convergence Analyses of Three-term Conjugate Gradient Methods, Mathematica Numericia Sinica, 21:3, pp. 355-362.

  9. Y.H. Dai (1999), Further Insight Into the Convergence of the Fletcher-Reeves Method, Science in China (Series A), 42:9, pp. 905-916.

  10. Y.H. Dai and Y. Yuan (1999), Global Convergence of the Method of Shortest Residuals, Numerische Mathematik, 83, pp. 581-598.

  11. Y.H. Dai (2000), Some Properties of Memoryless Quasi-Newton Method, Journal on Numerical Methods and Computer Applications, 1, pp. 28-32.

  12. Y.H. Dai and Y. Yuan (2000), Nonlinear Conjugate Gradient Methods, Shanghai Scientific and Technical Publishers, Shanghai. (in Chinese)

  13. Y.H. Dai and Y. Yuan (2001), An Extended Class of Nonlinear Conjugate Gradient Methods, In: D. Li eds. Proceedings of the 5th International Conference on Optimization: Techniques and Applications (December 2001, Hongkong), pp. 778-785.

  14. Y.H. Dai and H. Zhang (2001), An Adaptive Two-Point Stepsize Gradient Algorithm, Numerical Algorithms, 27, pp. 377-385.

  15. W.B. Liu and Y.H. Dai (2001), Minimization Algorithms Based on Supervisor and Searcher Cooperation, Journal of Optimization Theory and Applications, 111:2, pp. 359-379.

  16. Y.H. Dai and Y. Yuan (2001) An Efficient Hybrid Conjugate Gradient Method for Unconstrained Optimization, Annals of Operations Research, 103, pp. 33-47.

  17. Y.H. Dai (2001), Convergence of Nonlinear Conjugate Gradient Methods, Journal of Computational Mathematics, 19:5, pp. 539-548.

  18. Y.H. Dai and Y. Yuan (2001), A Three-parameter Family of Nonlinear Conjugate Gradient Methods, Mathematics of Computation, 70, pp. 1155-1167.

  19. Y.H. Dai and L.Z. Liao (2001), New Conjugacy Condition and Its Resulting Nonlinear Conjugate Gradient Methods, Applied Mathematics and Optimization, 43:1, pp. 87-101.

  20. Y.H. Dai (2001), New Properties of A Nonlinear Conjugate Gradient Method, Numerische Mathematics, 89:1, pp. 83-98.

  21. Y.H. Dai and Y. Yuan (2002), A Note on the Nonlinear Conjugate Gradient Method, Journal of Computational Mathematics, 20, pp. 575-582.

  22. Y.H. Dai (2002), A Nonmonotone Conjugate Gradient Algorithm for Unconstrained Optimization, Journal of Systems Science and Complexity, 15:2, 139-145.

  23. Y.H. Dai, J.Y. Yuan, and Y. Yuan (2002), Modified Two-point Stepsize Gradient Methods for Unconstrained Optimization, Computational Optimization and Applications, 22, 103-109.

  24. Y.H. Dai (2002), Convergence Properties of the BFGS algorithm, SIAM Journal on Optimization, 13:3, pp. 693-701.

  25. Y.H. Dai (2002), On the Nonmonotone Line Search, Journal of Optimization Theory and Applications, 112:2, pp. 315-330.

  26. Y.H. Dai and L.Z. Liao (2002), R-Linear Convergence of the Barzilai and Borwein Gradient Method, IMA Journal of Numerical Analysis, 22, 1-10.

  27. Y.H. Dai (2003), Unified Convergence Analyses of Nonlinear Conjugate Gradient Methods, in: Y.Yuan, ed. Numerical Linear Algebra and Optimization (Science Press, Beijing/NewYork, 2003) pp. 30-41.

  28. W.B. Liu, Y.H. Dai and J. Lamb (2003), Novel supervisor-searcher cooperation algorithms for minimization problems with strong noise, Optimization Methods and Software, 18, pp. 246-264.

  29. Y.H. Dai and Y. Yuan (2003), Alternate Minimization Gradient Method, IMA Journal of Numerical Analysis, 23, pp. 377-393.

  30. Y.H. Dai, J. M. Mart'inez, and J. Y. Yuan (2003), An Increasing-Angle Property of the Conjugate Gradient Method and the Implementation of Large-Scale Minimization Algorithms with Line Searches, Numerical Linear Algebra and Applications, 10:4, pp. 323-334.

  31. Y.H. Dai and Y. Yuan (2003), A Class of Globally Convergent Conjugate Gradient Methods, Sciences in China (series A) 46:2, pp. 251-261.

  32. Y.H. Dai and Q. Ni (2003), Testing Different Conjugate Gradient Methods for Large-Scale Unconstrained Optimization, Journal of Computational Mathematics, 21:3, pp. 311-320.

  33. Y.H. Dai and D.C. Xu (2003), A New Family of Trust Region Algorithms for Unconstrained Optimization, Journal of Computational Mathematics 21:2, pp. 221-228.

  34. Y.H. Dai (2003), Alternate Step Gradient Method, Optimization, 52:4-5, pp. 395-415.

  35. Y.H. Dai (2003), A Family of Hybrid Conjugate Gradient Methods for Unconstrained Optimization, Mathematics of Computation, 72, pp. 1317-1328.

  36. Y.H. Dai, L.Z. Liao and D. Li (2004), On Restart Procedures for the Conjugate Gradient Method, Numerical Algorithms, 35:2-4, pp. 249-260.

  37. Y.H. Dai, J.Y. Yuan (2004), Study on Semi-Conjugate Gradient Methods for Non-Symmetric Linear Systems, International Journal for Numerical Methods in Engineering, 60:8, pp. 1383-1399.

  38. Y.H. Dai and R. Fletcher (2005), Projected Barzilai-Borwein Methods for Large-Scale Box-Constrained Quadratic Programming, Numerische Mathematik, 100:1, pp. 21-47.

  39. Y.H. Dai and R. Fletcher (2005), On the asymptotic behaviour of some new gradient methods, Mathematical Programming, 103:3, pp. 541-559.

  40. Y. H. Dai and Y. Yuan (2005), Analyses of Monotone Gradient Methods, Journal of Industry and Management Optimization, Vol. 1, No. 2, pp. 181-192.

  41. Y.H. Dai, L.Z. Liao, D. Li (2005), An Analysis of Barzilai-Borwein Gradient Method for Unsymmetric Linear Equations, In: Optimization and control with applications (L. Qi, K. Teo and X. Yang, eds.), Springer, pp. 183-211.

  42. Y.H.Dai, W.W. Hager, K.Schittkowski, H.C.Zhang (2006), The cyclic Barzilai-Borwein Method for unconstrained optimization, IMA Journal of Numerical Analysis, 26:3, pp. 604-627.

  43. B.Zhou, L.Gao and Y. H. Dai (2006), Monotone Projected Gradient Methods for Large-Scale Box-Constrained Quadratic Programming, Science in China, Series A, 36:5, pp. 556-570.

  44. B.Zhou, L. Gao and Y. H. Dai (2006), Gradient Methods with Adaptive Step-Sizes, Computational Optimization and Applications, 35:1, pp. 69-86.

  45. Y.H. Dai and R. Fletcher (2006), New Algorithms for Singly Linearly Constrained Quadratic Programs Subject to Lower and Upper Bounds, Mathematical Programming, 106:3, pp. 403-421.

  46. Y.H. Dai (2006), Fast Algorithms on Projection on an Ellipsoid, SIAM Journal on Optimization, 16:4, pp. 986-1006.

  47. Y.H. Dai and X.Q. Yang (2006), A New Gradient Method with an Optimal Stepsize Property, Computational Optimization and Applications, 33:1, pp. 73-88.

  48. Y.Q.Hu and Y.H.Dai (2007), Inexact Barzilai-Borwein Method for Saddle Point Problems, Numerical Linear Algebra with Applications, 14, pp. 299-317.

  49. Z.Xu and Y.H.Dai (2008), A Stochastic Approximation Frame Algorithm with Adaptive Directions, Numerical Mathematics-Theory Methods and Applications, 1:4, pp. 460-474.

  50. L.P.Wang and Y.H.Dai (2008), Left Conjugate Gradient Methods for Non-Hermitian Linear Systems, Numerical Linear Algebra with Applications, 15, pp. 891-909.

  51. Y. H. Dai and K. Schittkowski (2008), A Sequential Quadratic Programming Algorithm with Non-Monotone Line Search, Pacific Journal of Optimization, 4:2, pp. 335-351.

  52. F. Li, Y. Fu, Y -H. Dai, C. Siminchisescu, J. Wang (2009), Kernel Learning by Unconstrained Optimization, Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, Florida, USA, Volume 1 of JMLR: WCP5, pp.328-335.

  53. G. H. Yu, L. Q. Qi and Y. H. Dai (2009), On Nonmonotone Chambolle Gradient Projection Algorithms for Total Variation Image Restoration, Journal of Mathematical Imaging and Vision, 35:2, pp. 143-154.

  54. Y.S. Fu and Y.H. Dai (2010), Improved Projected Gradient Algorithms for Singly Linearly Constrained Quadratic Programs Subject to Lower and Upper Bounds, Asia-Pacific Journal of Operational Research, 27:1, pp. 71-84.

  55. M.H.Cheng and Y.H.Dai (2010), Sparse Two-Sided Rank-One Updates for Nonlinear Equations, Sci. China Math, 53:11, pp. 2907-2915.

  56. Y.F. Liu, Y. H. Dai and Z.Q. Luo (2010), On the Complexity of Optimal Coordinated Downlink Beamforming, in: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Mar. 2010, pp. 3274-3277.

  57. Y. H. Dai (2011), Convergence Analysis of Nonlinear Conjugate Gradient Methods, In: Optimization and Regularization for Computational Inverse Problems and Applications (Ed. by Y.Wang, A.G.Yagola and C.Yang), Springer, pp. 157-181.

  58. Y. H. Dai(2010), Nonlinear Conjugate Gradient Methods, Wiley Encyclopedia of Operations Research and Management Science, Published Online, Feb 2011, DOI: 10.1002/ 9780470400531.eorms0183.

  59. Y.H.Dai (2011), Convergence of Conjugate Gradient Methods with Constant Stepsizes, Optimization Methods and Software, 26:6, pp.895-909.

  60. Y. F. Liu, Y. H. Dai and Z. Q. Luo (2011), Coordinated Beamforming for MISO Intereference Channel: Complexity Analysis and Efficient Algorithms, IEEE Transactions on Signal Processing, 59:3, pp.1142-1157.

  61. Y.-F. Liu, Y. H. Dai, and Z.-Q. Luo (2011), Max-min fairness linear transceiver design for a multi-user MIMO interference channel, in: Proc. IEEE International Conference on Communications, Jun. 2011, pp. 1-5.

  62. Y.-F. Liu, Y. H. Dai, and Z.-Q. Luo (2011), On the complexity of leakage interference minimization for interference alignment, in: Proc. IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications, Jun. 2011, pp. 471-475.

  63. Y. H. Dai and N. Yamashita (2011), Convergence analysis of sparse quasi-Newton updates with positive definite matrix completion for two-dimensional functions, Numerical Algebra, Control and Optimization, 1:1, pp. 61-69.

  64. M. Cheng, Y. H. Dai (2012), Adaptive Nonmonotone Spectral Residual Method for Large-scale Nonlinear Systems, Pacific Journal of Optimization, 8:1, pp. 15-25.

  65. Y. H. Dai (2012), A General Convergence Result for the BFGS Method, Computational Methods for Applied Inverse Problems (Ed. by Y. F. Wang, A. G. Yagola and C. C. Yang), De Gruyter and Higher Education Press, DOI: 10.1515/9783110259056, pp. 241-248.

  66. Z. Xu and Y. H. Dai (2012), New Stochastic Approximation Algorithms with Adaptive Step Sizes, Optimization Letters, 6, pp. 1831-1846.

  67. Y. H. Dai (2013), A Perfect Example for the BFGS Method, Mathematical Programming, Ser. A, 138:1-2, pp. 501-530.

  68. Y. H. Dai and C. X. Kou (2013), A Nonlinear Conjugate Gradient Algorithm with An Optimal Property and An Improved Wolfe Line Search, SIAM Journal on Optimization, 23:1, pp. 296-320.

  69. Y. N. Chen, Y. H. Dai, D. R. Han and W. Y. Sun (2013), Positive Semidefinite Generalized Diffusion Tensor Imaging via Quadratic Semidefinite Programming, SIAM Journal on Imaging Sciences, 6:3, pp. 1531-1552.

  70. Y. F. Liu, Y. H. Dai, Z. Q. Luo (2013), Max-Min Fairness Linear Transceiver Design for a Multi-User MIMO Interference Channel, IEEE Transactions on Signal Processing, 61:9, pp. 2413-2423.

  71. Y. F. Liu, Y. H. Dai, Z. Q. Luo (2013), Joint Power and Admission Control via Linear Programming Deflation, IEEE Transactions on Signal Processing, 61:6, pp. 1327-1338.

  72. Y. F. Liu, M. Hong, Y. H. Dai (2013), Max-Min Fairness Linear Transceiver Design Problem for a Multi-User SIMO Interference Channel Is Polynomial Time Solvable, IEEE Signal Processing Letters, 20:1, pp. 27-30.

  73. Y. F. Liu and Y. H. Dai (2013), Joint Power and Admission Control via \(P\)-Norm Ninimization Deflation, In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4789-4793.

  74. Y. H. Dai (2013), A New Analysis on the Barzilai-Borwein Gradient Method, Journal of Operations Research Society of China, 1:2, pp. 187-198.

  75. B. Jiang, Y. H. Dai (2013), Feasible Barzilai-Borwein-Like Methods for Extreme Symmetric Eigenvalue Problems, Optimization Methods and Software, 28:4, pp. 756-784.

  76. Y. H. Dai and N. Yamashita (2014), Analysis of Sparse Quasi-Newton Updates with Positive Definite Matrix Completion, Journal of the Operations Research Society of China, 2:1, pp. 39-56.

  77. Y. F. Liu and Y. H. Dai (2014), On the Complexity of Joint Subcarrier and Power Allocation for Multi-User OFDMA Systems, IEEE Transactions on Signal Processing, 62:3, pp. 583-596. https://doi.org/10.1109/TSP.2013.2293130

  78. C. F. Cui, Y. H. Dai, J. W. Nie (2014), All Real Eigenvalues of Symmetric Tensors, SIAM Journal on Matrix Analysis and Applications, 35:4, pp. 1582-1601. https://doi.org/10.1137/140962292

  79. C. X. Kou and Y. H. Dai (2015), A Modified Self-Scaling Memoryless Broyden-Fletcher-Goldfarb-Shanno Method for Unconstrained Optimization, Journal of Optimization Theory and Applications, 165:1, pp. 209-224. https://doi.org/10.1007/s10957-014-0528-4

  80. H. Gao, Y. H. Dai and X. J. Tong (2015), Barzilai-Borwein-Like Methods for The Extreme Eigenvalue Problem, Journal of Industrial and Management Optimization, 11, pp. 999-1019.https://doi.org/10.3934/jimo.2015.11.999

  81. C. L. Hao, C. F. Cui and Y. H. Dai (2015), A Sequential Subspace Projection Method for Extreme Z-Eigenvalues of Supersymmetric Tensors, Numerical Linear Algebra and Applications, 22:2, pp. 283-298. https://doi.org/10.1002/nla.1949

  82. B. Jiang, C. Cui and Y. H. Dai (2014), Unconstrained Optimization Models for Computing Several Extreme Eigenpairs of Real Symmetric Matrices, Pacific Journal of Optimization, 10:1, pp. 55-71.

  83. Y. H. Dai and X. W. Liu (2014), Advances in linear and nonlinear programming, Operations Research Transactions, 18:1, pp. 69-92 (in Chinese).

  84. Y. H. Dai, M. Al-Baali and X. Yang (2015), A positive Barzilai-Borwein-like stepsize and an extension for symmetric linear systems, Numerical Analysis and Optimization, pp. 59-75. https://doi.org/10.1007/978-3-319-17689-5_3

  85. B. Jiang and Y. H. Dai (2015), A Framework of Constraint Preserving Update Schemes for Optimization on Stiefel Manifold, Mathematical Programming, Ser. A, 153:2, pp. 535-575.

  86. Y.F. Liu, S. Ma, Y.H. Dai and S. Zhang (2016), A Smoothing SQP Framework for A Class of Composite \(L_q\) Minimization over Polyhedron, Mathematical Programming, Ser. A, 158, pp. 467-500.

  87. Y.N. Chen, Y.H. Dai and D.R. Han (2016), Fiber Orientation Distribution Estimation Using a Peaceman-Rachford Splitting Method, SIAM J. Imaging Sciences, 9:2, pp. 573-604.

  88. Y.H. Dai and C.X. Kou (2016), A Barzilai-Borwein Conjugate Gradient Method, Science China Mathematics, 59:8, pp. 1511-1524. https://doi.org/10.1007/s11425-016-0279-2.

  89. C. Tan, S. Ma, Y.H. Dai and Y. Qian (2016), Barzilai-Borwein Step Size for Stochastic Gradient Descent, Accepted by The 30th Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain.

  90. Z.W. Chen and Y.H. Dai (2016), A Line Search Exact Penalty Method Without Bi-objective Strategy for Nonlinear Constrained Optimization, Journal of Computational and Applied Mathematics, 300, pp. 245-258.

  91. F. Xu, M. Sun and Y.H. Dai (2016), An Adaptive Lagrangian Algorithm for Optimal Portofolio Deleveraging with Cross-impact, Journal of Systems Science and Complexity.

  92. Z.B. Li and Y.H. Dai (2016), Optimal Beamforming Design in the Reverberant Environment, Scientia Sinica Mathematica, 46:6, pp. 877-892.

  93. M. Liu, C. Cui, X. Tong and Y.H. Dai (2016), Algorithms, Softwares and Recent Developments of Mixed Integer Nonlinear Programming(in Chinese), Scientia Sinica Mathematica, 46:1, pp. 1-20 .

  94. M. Liu, X. Tong and Y.H. Dai (2016), Algorithms and Recent Developments of Packing Problems(in Chinese), Mathematica Numerica Sinica, 38:3, pp. 257-280

  95. G.X. Li, C.K. Chen, Z. Wan and Y.H. Dai (2016), Integer Programming Model for Transport Aircraft Fleet Cargo Loading, Journal on Numerical Methods and Computer Applications, 37:3, pp. 233-244 (in Chinese).

  96. Y.H. Dai, D. Han, X. Yuan and W. Zhang (2017), A Sequential Updating Scheme of Lagrange Multiplier for Separable Convex Programming, Mathematics of Computation, 86:303, pp. 315-343.

  97. R. Diao, Y.F. Liu and Y.H. Dai (2017), A New Fully Polynomial Time Approximation Scheme for the Interval Subset Sum Problem, Journal of Global Optimization, 68:4, pp. 749-775.

  98. F. M. Xu, Y. H. Dai, Z. H. Zhao and Z. B. Xu (2017), Efficient Projected Gradient Methods for A Class of \(L_0\) Constrained Optimization Problems, Accepted by Science China: Mathematics.

  99. Y.K. Huang, B. Li, Y. Kang, Y.H. Dai and J.J. Liu (2017), A Mixed Integer Model and An Algorithm for Steady-State Gas Network Optimization, Operations Research Transactions, 21:2, pp. 13-23 (in Chinese).

  100. Y.H. Dai, R. Diao and K. Fu (2018), Complexity Analysis and Algorithm Design of Pooling Problem, Journal of the Operations Research Society of China, 6:2, pp. 249-266.

  101. W.Y. Cheng and Y.H. Dai (2018), Gradient-based Method with Active Set Strategy for \(\ell_1\) optimization , Mathematics of Computation, 87, pp. 1283-1305.

  102. C. Kou, Z.W. Chen and Y.H. Dai (2018), An Augmented Lagrangian Trust Region Method with A Bi-Objective Strategy, Journal of Computational Mathematics, 36:3, pp. 331-350.

  103. F. Xu, M. Wang, Y.H. Dai and D. Xu (2018), A Sparse Enhanced Indexation Model with Chance and Cardinality Constraints, Journal of Global Optimization, 70:1, pp. 5-25.

  104. Z. Li, K. Yiu and Y.H. Dai (2018), On Sparse Beamformer Design with Reverberation, Applied Mathematical Modelling, 58, pp. 98-110.

  105. S. Chen, Y.H. Dai and F. Xu (2018), On the Study of Sparse Linear Programming, Mathematica Numerica Sinica, 40:4, pp. 339-353 (in Chinese).

  106. W.K. Chen, L. Chen, M.M. Yang, Y.H. Dai (2018), Generalized Coefficient Strengthening Cuts for Mixed Integer Programming, Journal of Global Optimization, 70, pp. 289-306.

  107. F. Xu, Y. H. Dai, Z. Zhao, Z. B. Xu (2019), Efficient Projected Gradient Methods for Cardinality Constrained Optimization, Science China: Mathematics, 62:2, pp. 245-268.

  108. L. Zeng, Y.H. Dai and Y. Huang (2019), Convergence Rate of Gradient Descent Method for Multi-Objective Optimization, Journal of Computational Mathematics, 37:5, pp. 689-703.

  109. O. Burdakov, Y.H. Dai and N. Huang (2019), Stabilized Barzilai-Borwein Method, Journal of Computational Mathematics, 37:6, pp. 916-936. https://doi.org/10.4208/jcm.1911-m2019-0171

  110. Z. Wang, Y.H. Dai and F. Xu (2019), A Robust Interior Point Method for Computing the Analytic Center of An Ill-Conditioned Polytope with Errors, Journal of Computational Mathematics, 37:6, pp. 843-865.

  111. L. Chen, Y.H. Dai and Z. Wei (2019), An Outer Approximation Method for A Class of Minimax Convex MINLP Problems, Journal of Nonlinear and Convex Analysis, 20:3, pp. 379-393.

  112. Z. Li, Y.H. Dai and H. Gao (2019), Alternating Projection Method for A Class of Tensor Equations, Journal of Computational and Applied Mathematics, 346, pp. 490-504. https://doi.org/10.1016/j.cam.2018.07.013

  113. Y.H. Dai, X.W. Liu and J. Sun (2020), A Primal-Dual Interior-Point Method Capable of Rapidly Detecting Infeasibility for Nonlinear Programs, Journal of Industrial and Management Optimization, 16:2, pp. 1009-1035. https://doi.org/10.3934/jimo.2018190

  114. X.W. Liu and Y.H. Dai (2020), A Globally Convergent Primal-Dual Interior-Point Relaxation Method for Nonlinear Programs, Mathematics of Computation, 89:323, pp. 1301-1329. https://doi.org/10.1090/mcom/3487

  115. Z. Liu, H. Liu and Y.H. Dai (2020), An Improved Dai-Kou Conjugate Gradient Algorithm for Unconstrained Optimization, Computational Optimization and Applications, 75, pp. 145-167. https://doi.org/10.1007/s10589-019-00143-4

  116. Y.H. Dai, Y. Huang, X.W. Liu (2019), A Family of Spectral Gradient Methods for Optimization, Computational Optimization and Applications, 74, pp. 43–65. https://doi.org/10.1007/s10589-019-00107-8

  117. Z. Chen, Y.H. Dai and J. Liu (2020), A Penalty-Free Method with Superlinear Convergence for Equality Constrained Optimization, Computational Optimization and Applications, 76, pp. 801–833. https://doi.org/10.1007/s10589-019-00117-6

  118. T. Liu, Z. Lu, X. Chen and Y.H. Dai (2020), An Exact Penalty Method for Semidefinite-Box Constrained Low-Rank Matrix Optimization Problems, IMA Journal of Numerical Analysis, 40:1, pp. 563-586. https://doi.org/10.1093/imanum/dry069

  119. N. Huang, Y.H. Dai, Q. Hu (2019), Uzawa Methods for A class of Block Three-by-Three Saddle-Point Problems, Numerical Linear Algebra with Applications, 26:6, e2265. https://doi.org/10.1002/nla.2265

  120. M. Yang, Y. Huang, Y.H. Dai and B. Li (2020), An Efficient Global Optimization Algorithm for Heated Oil Pipeline Problems, Industrial and Engineering Chemistry Research, 59:14, pp. 6638-6649. https://doi.org/10.1021/acs.iecr.0c00039

  121. Y. Zhang, Y.H. Dai, W. Han, Z. Li (2020), Smoothing Quadratic Regularization Method for Hemivariational Inequalities, Optimization, 69:10, pp. 2217-2240. https://doi.org/10.1080/02331934.2020.1712393

  122. Y. Huang, Y.H. Dai, X.W. Liu and H. Zhang (2020), Gradient Methods Exploiting Spectral Properties, Optimization Methods and Software, 35:4, pp. 681-705. https://doi.org/10.1080/10556788.2020.1727476

  123. Y. H. Dai, F. Jarre and F. Lieder (2020), On the Existence of Affine Invariant Descent Directions, Optimization Methods and Software, 35:5, pp. 938-954. https://doi.org/10.1080/10556788.2020.1740221

  124. W.K. Chen and Y.H. Dai (2020), On the Mixed 0-1 Knapsack Set with a GUB Constraint. PDF.

  125. W.K. Chen and Y.H. Dai (2019), Combinatorial Separation Algorithm for the Continuous Knapsack Problem with Divisible Capacities. https://arxiv.org/abs/1907.03162

  126. S.J. Chen, W.K. Chen, Y.H. Dai, J.H. Yuan, and H.S. Zhang (2020), Efficient Presolving Methods for Influence Maximization Problem in Social Networks.

  127. L. Chen, W.K. Chen, M.M. Yang, and Y.H. Dai (2020) An Exact Separation Algorithm for Unsplittable Flow Capacitated Network Design Arc-set Polyhedron, Journal of Global Optimization, https://doi.org/10.1007/s10898-020-00967-z.

  128. K. Liao, Y. Hu, X. Hou, L. Wu, J. Wang, Y. Zhang, Y.H. Dai and Z. Yang (2020), The Quantitative Analysis of Causality between Population Migration and the Number of Newly Confirmed Cases (in Chinese), Mathematical Modeling and Its Applications, 9:1, pp. 24-29.

  129. Z. Ding, Y. Liu, J. Kong, H. Zhang, Y. Zhang, Y.H. Dai and Z. Yang (2020), A Probability Model for Estimating the Expected Number of the Newly Infected and Predicting the Trend of the Diagnosed (in Chinese), Operations Research Transactions, 24:1, pp. 1-12. https://doi.org/10.15960/j.cnki.issn.1007-6093.2020.01.001

  130. Y. Hu, J. Kong, L. Yang, X. Wang, Y. Zhang, Y.H. Dai and Z. Yang (2020), A Dynamic Growth Rate Model and Its Application in Global Epidemic Analysis, Acta Mathematicae Applications Sinica, 43:1, pp. 1-15.

  131. Y. Hu, Y. Liu, L. Wu, J. Wang, J. Dong, Y. Zhang, Y.H. Dai and Z. Yang (2020), A Dynamic Transmission Rate Model and Its Application in Epidemic Analysis, Operations Research Transactions.

  132. W. Peng, Y.H. Dai, H. Zhang and L. Cheng (2020), Training GANs with Centripetal Acceleration, Optimization Methods and Software, 35:5, pp. 955-973. https://doi.org/10.1080/10556788.2020.1754414

  133. H. Zhang, L. Guo, Y.H. Dai and W. Peng (2019), Proximal-Like Incremental Aggregated Gradient Method with Linear Convergence under Bregman Distance Growth Conditions, accepted by Mathematics of Operations Research. https://doi.org/10.1287/moor.2019.1047

  134. Y.H. Dai and L.W. Zhang (2020), Optimization with Least Constraint Violation, https://arxiv.org/abs/2010.02493v1.

  135. W. Chen and Y.H.Dai (2021), On the Complexity of Sequentially Lifting Cover Inequalities for the Knapsack Polytope, Science China Mathematics 64(1), pp. 211-220.

  136. Y.H. Dai and L.W. Zhang (2021), Optimization with Least Constraint Violation, CSI-AM Transactions on Applied Mathematcis, 2, pp. 551-584.

  137. Y. Huang, Y.H. Dai and X.W. Liu (2021), Equipping the Barzilai-Borwein Method with the Two-Dimensional Quadratic Termination Property, SIAM Journal on Optimization, 31:4, pp. 3068-2096.

  138. W. Cheng and Y.H. Dai (2021), An Active Set Newton-CG Method for L1 Optimization, Applied and Computational Harmonic Analysis, 50, pp. 303–325.

  139. Y.H. Dai, Z.H, Wang, F.M. Xu (2021), A Primal-Dual Algorithm for Unfolding Neutron Energy Spectrum from Multiple Activation Foils, Journal of Industrial and Management Optimization, 17, pp. 2367-2387.

  140. Z.L. Dong, J.M. Peng, F.M. Xu and Y.H. Dai (2021). On Some Extended Mixed Integer Optimization Models of The Eisenberg–Noe Model in Systemic Risk Management, International Transactions in Operational Research, 28, pp. 3014–3037.

  141. T.T. Yu, X.W. Liu, Y.H. Dai and J. Sun (2021), Stochastic Variance Reduced Gradient Methods Using a Trust-Region-Like Scheme, Journal of Scientific Computing, 87.

  142. T.T. Yu, X.W. Liu, Y.H. Dai and J. Sun (2021), A Minibatch Proximal Stochastic Recursive Gradient Algorithm Using a Trust-Region-Like Scheme and Barzilai–Borwein Stepsizes, IEEE Transactions on Neural Networks And Learning Systems, 32, pp. 4627-4638.

  143. J.W. Chen, Y.H. Dai (2022), Multiobjective Optimization with Least Constraint Violation: Optimality Conditions and Exact Penalization, Journal of Global Optimization, https://doi.org/10.1007/s10898-022-01158-8.

  144. X.X. Ju, C.D. Li, Y.H. Dai and J.W. Chen (2022), A New Dynamical System with Self-adaptive Dynamical Stepsize for Pseudomonotone Mixed Variational Inequalities, Optimization, DOI: 10.1080/02331934.2022.2094795.

  145. Z.W. Chen, Y.H. Dai, T.Y. Zhang (2022), A Line Search Penalty-free SQP Method for Equality-constrained Optimization without Maratos Effect, IMA Journal of Numerical Analysis (2022) 00, 1–32.

  146. J. Gao, X.W. Liu, Y.H. Dai, Yakui Huang and Peng Yang (2022), Achieving Geometric Convergence for Distributed Optimization with Barzilai-Borwein Step Sizes, SCIENCE CHINA: Information Sciences, (2022)65 149204:1–149204:2.

  147. Y.K. Huang, Y.H. Dai, X.W. Liu, Hongchao Zhang (2022), On the Acceleration of The Barzilai–Borwein Method, Computational Optimization and Applications, https://doi.org/10.1007/s10589-022-00349-z.

  148. Y.K. Huang, Y.H. Dai, X.W. Liu, Hongchao Zhang (2022), On The Asymptotic Convergence and Acceleration of Gradient Methods, Journal of Scientific Computing, https://doi.org/10.1007/s10915-021-01685-8.

  149. X.W. Liu, Y.H. Dai, Y.K. Huang (2022), A Primal-Dual Interior-Point Relaxation Method with Global and Rapidly Local Convergence for Nonlinear Programs, Mathematical Methods of Operations Research, https://doi.org/10.1007/s00186-022-00797-7.

  150. T.T. Yu, X.W. Liu, Y.H. Dai (2022), J. Sun, A Mini-Batch Proximal Stochastic Recursive Gradient Algorithm with Diagonal Barzilai–Borwein Stepsize, Journal of the Operations Research Society of China, https://doi.org/10.1007/s40305-022-00436-2.

  151. L. Chen, Y.H. Dai, Z. Wei (2022), Sufficient Conditions for Existence of Global Minimizers of Functionson Hilbert Spaces, Journal of Global Optimization, https://doi.org/10.1007/s10898-022-01133-3.

  152. Y.H. Dai and L.W. Zhang (2023), Stability for Constrained Minimax Optimization, CSIAM Transactions on Applied Mathematics, 4(3), pp. 542-565.

  153. J. Gao, X.W. Liu, Y.H. Dai, Y.K. Huang and P. Yang(2023), A Family of Distributed Momentum Methods Over Directed Graphs With Linear Convergence, IEEE Transactions on Automatic Control, 68(2), pp. 1085-2023.

  154. F.M. Xu, X.P. Li, Y.H. Dai and M.H. Wang(2023), New Insights and Augmented Lagrangian Algorithm for Optimal Portfolio Liquidation with Market Impact, International Transactions In Operational Research, 30, pp. 2640-2664.

  155. H. Zhang, Y.H. Dai(2023), Mirror Frameworks for Relatively Lipschitz and Monotone-Like Variational Inequalities, Journal of the Operations Research Society of China, https://doi.org/10.1007/s40305-023-00458-4.

  156. X.W. Liu, Y.H. Dai, Y.K. Huang and J. Sun(2023), A Novel Augmented Agrangian Method of Multipliers for Optimization with General Inequality Constraints, Mathematics of Computation, 92(341), pp. 1301-1330.

  157. Y.H. Dai, L.W. Zhang(2023), The augmented Lagrangian method can approximately solve convex optimization with least constraint violation, Mathematical Programming 200, pp. 633-667.

  158. W.K. Chen, Y.F. Liu, F. Liu, Y.H. Dai and Z.Q. Luo(2023), Towards Efficient Large-Scale Network Slicing: An LP Dynamic Rounding-and-Refinement Approach, IEEE Transactions on Signal Processing, 71, pp. 615-630.