# Papers

## Preprints

1. M.X. Lin,  D.F. Sun, K.C. Toh, and Y.C. Yuan,  A dual Newton based preconditioned proximal point algorithm for exclusive lasso models,
arXiv:1902.00151
2. Y.J. Zhang, N. Zhang, D.F. Sun, and K.C. Toh, A proximal point dual Newton algorithm for solving group graphical Lasso problems,
arXiv:1906.04647, 2019.
3. S.Y. Kim, M. Kojima, and K.C. Toh, A Newton-bracketing method for a simple conic optimization problem,
arXiv:1905.12840, 2019.
4. X.D. Li, D.F. Sun, and K.C. Toh, An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for Linear Programming,
arXiv:1903.09546, 2019.
5. P.P. Tang, C.J. Wang, D.F. Sun, and K.C. Toh, A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems,
arXiv:1903.11460, 2019.
6. N. Zhang, Y.J. Zhang, D.F. Sun, and K.C. Toh, An efficient linearly convergent regularized proximal point algorithm for fused multiple graphical Lasso problems,
arXiv:1902.06952, 2019.
7. X.Y. Lam, D.F Sun, and K.C. Toh, A semi-proximal augmented Lagrangian based decomposition method for primal block angular convex composite quadratic conic programming problems,
arXiv:1812.04941, 2018.
8. T.-D. Quoc, L. Liang, K.C. Toh, A new homotopy proximal variable-metric framework for composite convex minimization,
arXiv:1812.05243, 2018.
9. D.F. Sun, K.C. Toh, and Y.C. Yuan, Convex clustering: model, theoretical guarantee and efficient algorithm,
arXiv:1810.02677, 2018.
10. L. Yang, J. Li, D.F. Sun, and K.C. Toh, A fast globally linearly convergent algorithm for the computation of Wasserstein barycenters,
arXiv:1809.04249, 2018.
11. Y. Cui, D.F. Sun and K.C. Toh, On the asymptotic superlinear convergence of the augmented Lagrangian method for semidefinite programming with multiple solutions,
arXiv:1610.00875, 2016.
12. S.Y. Kim, M. Kojima, and K.C. Toh, Doubly nonnegative relaxations for quadratic and polynomial optimization problems with binary and box constraints,
preprint at Optimization Online, 2016.

## 2017–present

1. S.Y. Kim, M. Kojima, and K.C. Toh, A geometrical analysis of a class of nonconvex conic programs for convex conic reformulations of quadratic and polynomial optimization problems,
SIAM J. Optimization, in print, 2020.
arXiv:1901.02179.
2. S.Y. Kim, M. Kojima, and K.C. Toh, Doubly nonnegative relaxations are equivalent to completely positive reformulations of quadratic optimization problems with block-clique graph structures,
J. Global Optimization, in print, 2020. arXiv:1903.07325.
3. C. Ding, D.F. Sun, J. Sun, and K.C. Toh, Spectral operators of matrices: semismoothness and characterizations of the generalized Jacobian,
SIAM J. Optimization, in print, 2020.
arXiv:1810.09856.
4. L. Chen, X.D. Li, D.F. Sun, and K.C. Toh, On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming,
Mathematical Programming, in print, 2020. arXiv:1803.10803.
5.  X.D. Li, D.F. Sun and K.C. Toh, On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope,
Mathematical Programming, 179 (2020),  pp. 419–446.
arXiv:1702.05934. Springer Nature ShareIt.
6. Y.J. Zhang, N. Zhang, D.F. Sun and K.C. Toh, An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems,
Mathematical Programming, 179 (2020), pp. 223–263. arXiv:1712.05910. Springer Nature ShareIt.
7. D.F. Sun, K.C. Toh, Y.C. Yuan, and X.Y. Zhao, SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0),
Optimization Methods and Software, 35 (2020), 87–115. arXiv:1710.10604.
8. S.L. Hu, D.F. Sun, and K.C. Toh, Best nonnegative rank-one approximations of tensors,
SIAM J. Matrix Analysis and Applications, 40 (2019), pp. 1527–1554.  arXiv:1810.13372.
9. Y. Cui, D.F. Sun, and K.C. Toh, Computing the best approximation over the intersection of a polyhedral set and the doubly nonnegative cone,
SIAM J. Optimization, 29 (2019), pp. 2785–2813. arXiv:1803.06566.
10. Z.Y. Lou, D.F. Sun, K.C. Toh, and N.H. Xiu, Solving the OSCAR and SLOPE models using a semismooth Newton-based augmented Lagrangian method,
J. Machine Learning Research, 20 (2019), Article 106. arXiv:1803.10740.
11. M.X. Lin, Y.J. Liu, D.F. Sun, and K.C. Toh, Efficient sparse Hessian based algorithms for the clustered Lasso problem,
SIAM J. Optimization, 29 (2019), pp. 2026–2052. arXiv:1808.07181.
12. L. Chen, D.F. Sun, K.C. Toh, and N. Zhang, A unified algorithmic framework of symmetric Gauss-Seidel decomposition based proximal ADMMs for convex composite programming,
J. Computational Mathematics, 37 (2019), pp. 739–757. arXiv:1812.06579.
13. N. Ito, S. Kim, M. Kojima, A. Takeda, and K.C. Toh, BBCPOP: A sparse doubly nonnegative relaxation of polynomial optimization problems with binary, box and complementarity constraints,
ACM Transactions on Mathematical Software, 45 (2019), Article 34.
arXiv:1804.00761. BBCPOP Matlab Software.
14. N. Arima, S.Y. Kim, M. Kojima, and K.C. Toh, Lagrangian-conic relaxations, Part II: Applications to polynomial optimization problems,
Pacific J. Optimization, 15 (2019), pp. 415–439. Preprint at Optimization Online.
15. L. Chen, D.F. Sun and K.C. Toh, Some problems on the Gauss-Seidel iteration method in degenerate cases, (in Chinese)
Journal On Numerical Methods and Computer Applications, 40 (2019), pp. 98–110.
16. Y. Cui, D.F. Sun and K.C. Toh, On the R-superlinear convergence of the KKT residuals generated by the augmented Lagrangian method for convex composite conic programming,
Mathematical Programming, 178 (2019), pp. 381–415.  arXiv:1706.08800. Springer Nature SharedIt.
17. X.D. Li, D.F. Sun and K.C. Toh, A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications,
Mathematical Programming, 175 (2019), pp. 395–418. arXiv:1703.06629. Springer Nature SharedIt.
18. N. Ito, S. Kim, M. Kojima, A. Takeda, and K.C. Toh, Equivalences and differences in conic relaxations of combinatorial quadratic optimization problems,
J. Global Optimization, 72 (2018), pp. 619–653. Preprint at Optimization Online. Springer Nature SharedIt.
19. X.D. Li, D.F. Sun and K.C. Toh, On efficiently solving the subproblems of a level-set method for fused lasso problems,
SIAM J. Optimization, 28 (2018), pp. 1842–1866.
arXiv:1706.08732. Detailed computational results in the paepr.
20. X.D. Li, D.F. Sun, and K.C. Toh, QSDPNAL: A two-phase augmented Lagrangian method for convex quadratic semidefinite programming,
Mathematical Programming Computation, 10 (2018), pp. 703–743. arXiv:1512.08872. Springer Nature SharedIt.
21. K. Natarajan, D.J. Shi, and K.C. Toh, Bounds for random binary quadratic programs,
SIAM J. Optimization, 28 (2018), pp. 671–692.
22. X.D. Li, D.F. Sun, and K.C. Toh, A highly efficient semismooth Netwon augmented Lagrangian method for solving Lasso problems,
SIAM J. Optimization, 28 (2018), pp. 433–458.
arXiv:1607.05428.
23. Z.W. Li, L.F. Cheong, S.G. Yang, and K.C. Toh, Simultaneous clustering and model selection: algorithm, theory and applications,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 40 (2018), pp. 1964–1978.
24. X.Y. Lam, J.S. Marron, D.F. Sun, and K.C. Toh, Fast algorithms for large scale generalized distance weighted discrimination,
J. Computational and Graphical Statistics, 27 (2018), pp. 368–379. arXiv:1604.05473. R package.
25. T. Weisser, J.B. Lasserre, and K.C. Toh, A bounded degree SOS hierarchy for large scale polynomial optimization with sparsity,
Mathematical Programming Computation, 10 (2018), pp. 1–32. arXiv:1607.01151. Springer Nature SharedIt.
26. C. Ding, D.F. Sun, J. Sun, and K.C. Toh, Spectral operators of matrices,
Mathematical Programming, 168 (2018), pp. 509–531. arXiv:1401.2269.
27. Ethan Fang, H. Liu, K.C. Toh, W.-X. Zhou, Max-norm optimization for robust matrix recovery,
Mathematical Programming, 167 (2018), pp. 5–35. Preprint at Optimization Online. Springer Nature SharedIt.
28. N. Arima, S.Y. Kim, M. Kojima, and K.C. Toh, Lagrangian-conic relaxations, Part I: A unified framework and its applications to quadratic optimization problems,
Pacific J. Optimization, 14 (2018), pp.161–192. Preprint at Optimization Online.
29. N. Ito, A. Takeda, and K.C. Toh,
J. Machine Learning Research, 18 (2017), article 16, pp.1–49.
30. N. Arima, S.Y. Kim, M. Kojima, and K.C. Toh, A robust Lagrangian-DNN method for a class of quadratic optimizaiton problems,
Computational Optimization and Applications, 66 (2017), pp. 453–479. Preprint at Optimization Online.
31. L. Chen, D.F. Sun, and K.C. Toh, A note on the convergence of ADMM for linearly constrained convex optimization problems,
Computational Optimization and Applications, 66 (2017), pp. 327—343.  arXiv:1507.02051
32. J.B. Lasserre, K.C. Toh, and S.G. Yang, A bounded-SOS-hierarchy for polynomial optimization,
EURO J. Computational Optimization, 5 (2017), pp. 87–117. arXiv:1501.06126.
33. L. Chen, D.F. Sun, and K.C. Toh, An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming,
Mathematical Programming, 161 (2017), pp. 237–270. arXiv:1506.00741. Springer Nature SharedIt.

## 2014–2016

34. D.F. Sun, K.C. Toh, and L.Q. Yang, An efficient inexact ABCD method for least squares semidefinite programming,
SIAM J. Optimization, 26 (2016), pp. 1072–1100.
arXiv:1505.04278.
Detailed computational results for over 600 problems tested in the paper.
35. Y. Cui, X.D. Li, D.F. Sun and K.C. Toh, On the convergence properties of a majorized ADMM for linearly constrained convex optimization problems with coupled objective functions,
J. Optimization Theory and Applications, 169 (2016), pp. 1013–1041.
arXiv:1502.00098. Springer Nature SharedIt
36. M. Li, D.F. Sun, and K.C. Toh, A majorized ADMM with indefinite proximal terms for linearly constrained convex composite optimization,
SIAM J. Optimization, 26 (2016), pp. 922–950.
arXiv:1412.1911.
37. S.Y. Kim, M. Kojima, and K.C. Toh, A Lagrangian-DNN relaxation: a fast method for computing tight lower bounds for a class of quadratic optimization problems,
Mathematical Programming, 156 (2016), pp. 161–187.
38. C.H. Chen, Y.J. Liu, D.F. Sun, and K.C. Toh, A semismooth Newton-CG dual proximal point algorithm for spectral norm approximation problems,
Mathematical Programming, 155 (2016), pp. 435–470.
39. X.D. Li, D.F. Sun and K.C. Toh, A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions ,
Mathematical Programming, 155 (2016), pp. 333–373.
arXiv:1409.2679.
40. L.Q. Yang, D.F. Sun, and K.C. Toh, SDPNAL+: a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints,
Mathematical Programming Computation, 7 (2015), pp. 331-366.
arXiv:1406.0942.
More recent computational results (computed in Dec 2017).
Numerical experiments on a variety of large scale SDPs with the matrix dimension nn $n$ up to 9,2619,261 $9,261$ and the number of equality constraints mm $m$ up to 12,326,39012,326,390 $12,326,390$ show that the proposed method is very efficient on certain large SDPs. We are also able to solve the SDP problem fap36 (with n=4,110n=4,110 $n = 4,110$ and m=1,154,467m=1,154,467 $m = 1,154,467$ ) in the Seventh DIMACS Implementation Challenge much more efficiently (in 23 hours in 2015) and accurately than previous attempts. The approximate optimal objective value we obtained for fap36 is 69.85, with the corresponding solution having relative primal and dual infeasibilities, and complementarity gap ⟨X,S⟩ $\langle X,S\rangle$ all less than 1e-6.
41. D.F. Sun, K.C. Toh and L.Q. Yang, A convergent 3-block semi-proximal alternating direction method of multipliers for conic programming with 4-type constraints,
SIAM J. Optimization, 25 (2015), pp. 882–915.
arXiv:1404.5378.
Detailed computational results for over 400 problems tested in the paper.
Supplementary note: more detailed comparison between the performance of our algorithm and various variants of ADMMs.
42. M. Li, D.F. Sun, and K.C. Toh, A convergent 3-block semi-proximal ADMM for convex minimization with one strongly convex block,
Asia Pacific J. Operational Research, 32 (2015), 1550024. arXiv:1410.7933.
43. Y.X. Wang, C.M. Lee, L.F. Cheong, and K.C. Toh, Practical matrix completion and corruption recovery using proximal alternating robust subspace minimization,
International J. of Computer Vision, 111 (2015), pp. 315–344. arXiv:1309.1539.
44. C. Tang, K.K. Phoon, and K.C. Toh, Effect of footing width on Ny and failure envelope of eccentrically and obliquely loaded strip footings on sand,
Canadian Geotechnical Journal, 52 (2015), pp. 694–707.
45. J. Peng, T. Zhu, H. Luo, and K.C. Toh, Semidefinite relaxation of quadratic assignment problems based on nonredundant matrix splitting,
Computational Optimization and Applications, 60 (2015), pp. 171–198.
46. K.F. Jiang, D.F. Sun, and K.C. Toh, A partial proximal point algorithm for nuclear norm regularized matrix least squares problems,
Mathematical Programming Computation, 6 (2014), pp. 281–325.
47. Z. Gong, Z.W. Shen, and K.C. Toh, Image restoration with mixed or unknown noises,
Multiscale Modeling and Simulation, 12 (2014), pp. 458–487.
48. B. Wu, C. Ding, D.F. Sun, and K.C. Toh, On the Moreau-Yoshida regularization of the vector k-norm related functions,
SIAM J. Optimization, 24 (2014), pp. 766–794.
49. K. Natarajan, D.J. Shi, and K.C. Toh, A probabilistic model for minimax regret in combinatorial optimization,
Operations Research, 62 (2014), pp. 160–181.
50. C. Ding, D.F Sun and K.C. Toh, An introduction to a class of matrix cone programming,
Mathematical Programming, 144 (2014), pp. 141–179.
51. C. Tang, K.K. Phoon, and K.C. Toh, Lower bound limit analysis for seismic passive earth pressure on rigid walls,
International J. of Geomechanics, 14 (2014), 04014022.
52. C. Tang, K.C. Toh, and K.K. Phoon, Axisymmetric lower bound limit analysis using finite elements and second-order cone programming,
J. of Engineering Mechanics, 140 (2014), pp. 268–278.

## 2011–2013

53. Z.Z. Zhang, G.L. Li, K.C. Toh, and W.K. Sung, 3D chromosome modeling with semi-definite programming and Hi-C data,
J. Computational Biology, 20 (2013), pp. 831–846.
54. J.F. Yang, D.F. Sun, and K.C. Toh, A proximal point algorithm for log-determinant optimization with group Lasso regularization,
SIAM J. Optimization, 23 (2013), pp. 857–893.
55. X.V. Doan, K.C. Toh, and S. Vavasis, A proximal point algorithm for sequential feature extraction applications,
SIAM J. Scientific Computing, 35 (2013), pp. 517–540.
56. T.H.H. Tran, K.C. Toh, and K.K. Phoon, Preconditioned IDR(s) iterative solver for non-symmetric linear system associated with FEM analysis of shallow foundation,
International J. for Numerical and Analytical Methods in Geomechanics, 37 (2013), pp. 2972–2986.
57. K. B. Chaudhary, K.K. Phoon, and K.C. Toh, Inexact block diagonal preconditioners to mitigate the effects of relative differences in material stiffnesses,
International J. Geomechanics, 13 (2013), pp. 273–291.
58. K. B. Chaudhary, K.K. Phoon, and K.C. Toh, Effective block diagonal preconditioners for Biot’s consolidation equations in piled-raft foundations,
International J. Numerical and Analytical Methods in Geomechanics, 37 (2013), pp. 871–892.
59. K.F. Jiang, D.F. Sun, and K.C. Toh,
SIAM J. Optimization, 22 (2012), pp. 1042–1064.
60. Y.J. Liu, D.F. Sun, and K.C. Toh,
Mathematical Programming, 133 (2012), pp. 399–436.
61. X.Y. Zhao, and K.C. Toh, Infeasible potential reduction algorithms for semidefinite programming,
Pacific J. Optimization, 8 (2012), pp. 725–753.
62. X. Chen, K.K. Phoon, and K.C. Toh, Performance of zero-level fill-in preconditioning techniques for iterative solutions in geotechnical applications,
International J. Geomechanics, 12 (2012), pp. 596–605.
63. Z. Shen, K.C. Toh, and S. Yun, An accelerated proximal gradient algorithm for frame based image restoration via the balanced approach,
SIAM J. Imaging Sciences, 4 (2011), pp. 573–596.
64. S. Yun, P. Tseng, and K.C. Toh, A block coordinate gradient descent method for regularized convex separable optimization and covariance selection,
Mathematical Programming, 129 (2011), pp. 331–355.
65. L. Li, and K.C. Toh, A polynomial-time inexact primal-dual infeasible path-following algorithm for convex quadratic SDP,
Pacific J. Optimization, 7 (2011), pp. 43–61.
66. S. Yun, and K.C. Toh, A coordinate gradient descent method for L1-regularized convex minimization ,
Computational Optimization and Applications, 48 (2011), pp. 273–307.
Erratum: In Lemma 3.4, add Assumption 2 so that equation (22) is valid.

## 2008–2010

67. K.C. Toh, and S.W. Yun An accelerated proximal gradient algorithm for nuclear norm regularized least squares problems,
Pacific J. Optimization, 6 (2010), pp. 615–640.
Numerical results suggest that our algorithm is efficient and robust in solving large-scale random matrix completion problems. In particular, we are able to solve random matrix completion problems with matrix dimensions up to $10^5$ each in less than 10 minutes on a modest PC.
68. Lu Li and K.C. Toh An inexact interior point method for L1-regularized sparse covariance selection,
Mathematical Programming Computation, 2 (2010), pp. 291–315.
69. L. Li, and K.C. Toh, A polynomial-time inexact interior-point method for convex quadratic symmetric cone programming,
J. Math-for-industry, 2 (2010), pp. 199–212.
70. X.-W. Liu, G.Y. Zhao, and K.C. Toh, On the implementation of a log-barrier progressive hedging method for multistage stochastic programs,
J. of Computational and Applied Mathematics, 234 (2010), pp. 579–592.
71. C.J. Wang, D.F. Sun, and K.C. Toh, Solving log-determinant optimization problems by a Newton-CG primal proximal point algorithm,
SIAM J. Optimization, 20 (2010), pp. 2994–3013.
Matlab software LogdetPPA
72. X.Y. Zhao, D.F. Sun, and K.C. Toh, A Newton-CG augmented Lagrangian method for semidefinite programming,
SIAM J. Optimization, 20 (2010), pp. 1737–1765.
Numerical experiments on a variety of large scale SDPs with the matrix dimension n up to 4,110 and the number of equality constraints m up to 2,156,544 show that the proposed method is very efficient on certain large SDPs. We are also able to solve the SDP problem fap36 (with n = 4,110 and m = 1,154,467) in the Seventh DIMACS Implementation Challenge much more accurately than previous attempts. The approximate optimal objective value we obtained for fap36 is 69.85, with the corresponding solution having relative primal and dual infeasibilities, and complementarity gap (Tr(XS)) all less than 1e-6.
73. N.-H. Z. Leung and K.-C. Toh, An SDP-based divide-and-conquer algorithm for large scale noisy anchor-free graph realization ,
SIAM J. Scientific Computing, 31 (2010), pp. 4351–4372.
A movie showing how the divide-and-conquer algorithm computes the conformation of a protein molecule.
74. P. Biswas, K.C. Toh, and Y. Ye, A distributed SDP approach for large scale noisy anchor-free graph realization with applications to molecular conformation ,
SIAM J. Scientific Computing, 30 (2008), pp. 1251–1277.
75. K.C. Toh, An inexact primal-dual path-following algorithm for convex quadratic SDP,
Mathematical Programming, 112 (2008), pp. 221–254.
76. K.C. Toh, and K.K. Phoon, Comparison between iterative solution of symmetric and non-symmetric forms of Biot’s FEM equations using the generalized Jacobi preconditioner,
International J. for Numerical and Analytical Methods in Geomechanics, 32 (2008), pp. 1131–1146.

## 2005–2007

77. X. Chen, K.K. Phoon, and K.C. Toh, Partitioned versus global Krylov subspace iterative methods for FE solution of 3-D Biot’s problem,
Computer Methods in Applied Mechanics and Engineering, 196 (2007), pp. 2737–2750.
78. J.S. Chai, and K.C. Toh, Preconditioning and iterative solution of symmetric indefinite linear systems arising from interior point methods for linear programming,
Computational Optimization and Applications, 36 (2007), pp. 221–247.
79. K.C. Toh, R.H. Tutuncu, and M.J. Todd, Inexact primal-dual path-following algorithms for a special class of convex quadratic SDP and related problems,
Pacific J. Optimization (special issue dedicated to Masakazu Kojima’s 60th birthday), 3 (2007), pp. 135–164.
80. R.M. Freund, F. Ordonez, and K.C. Toh, Behavioral measures and their correlation with IPM iteration counts on semi-definite programming problems,
Mathematical Programming, 109 (2007), pp. 445–475.
81. Z. Cai and K.C. Toh, Solving second order cone programming via the augmented systems,
SIAM J. Optimization, 17 (2006), pp. 711–737.
82. P. Biswas, T.C. Liang, K.C. Toh, T.C. Wang, and Y. Ye, Semidefinite programming approaches for sensor network localization with noisy distance measurements,
IEEE Transactions on Automation Science and Engineering, regular paper, 3 (2006), pp. 360–371.
83. X. Chen, K.C. Toh, and K.K. Phoon, A modified SSOR preconditioner for sparse symmetric indefinite linear systems of equations,
International J. Numerical Methods in Engineering, 65 (2006), pp. 785–807.
84. J.S. Chai and K.C. Toh, Computation of condition numbers for linear programming problems using Pena’s method,
Optimization Methods and Software, 21 (2006), pp. 419–443.
85. G.L. Zhou, and K.C. Toh, Superlinear convergence of a Newton-type algorithm for monotone equations,
J. Optimization Theory and Applications, 125 (2005), pp. 205–221.
86. G.L. Zhou, K.C. Toh, and J. Sun, Efficient algorithms for the smallest enclosing ball problem,
Computational Optimization and Applications, 30 (2005), pp. 147–160.

## 2002–2004

87. K.K. Phoon, K.C. Toh, and X. Chen, Block constrained versus generalized Jacobi preconditioners iterative solution of large-scale Biot’s FEM equations,
Computers and Structures, 82 (2004), pp. 2401–2411.
88. K.C. Toh, K.K. Phoon, and S.H. Chan, Block preconditioners for symmetric indefinite linear systems,
International J. Numerical Methods in Engineering, 60 (2004), pp. 1361–1381.
89. S. K. Chua, K. C. Toh and G. Y. Zhao, An analytic center cutting plane method with deep cuts for semidefinite feasibility problems,
J. Optimization Theory and Applications, 123 (2004), pp. 291–318.
90. G.L. Zhou, K.C. Toh, and G.Y. Zhao, Convergence analysis of an infeasible interior point algorithm based on a regularized central path for linear complementarity problems,
Computational Optimization and Applications, 27 (2004), pp. 269–283.
91. K. C. Toh, Solving large scale semidefinite programs via an iterative solver on the augmented systems,
SIAM J. Optimization, 14 (2004), pp. 670–698.
92. G.L. Zhou, and K.C. Toh, Polynomiality of an inexact infeasible interior point algorithm for semidefinite programming,
Mathematical Programming, 99 (2004), pp. 261–282.
93. K.K. Phoon, K.C.Toh, S.H. Chan, and F.H. Lee, Fast iterative solution of large undrained soil-structure interaction problems,
International J. for Numerical and Analytical Methods in Geomechanics, 27 (2003), pp. 159–181.
94. G.L. Zhou, K.C. Toh, and D.F. Sun, A globally and quadratically convergent algorithm for minimizing a sum of Euclidean norms,
J. Optimization Theory and Applications, 119 (2003), pp. 357–377.
95. R.H Tutuncu, K.C. Toh, and M.J. Todd, Solving semidefinite-quadratic-linear programs using SDPT3,
Mathematical Programming, 95 (2003), pp. 189–217.
96. K.C. Toh, G.Y Zhao, and J. Sun, A multiple-cut analytic center cutting plane method for semidefinite feasibility problems,
SIAM J. Optimizaton, 12 (2002), pp. 1126–1146.
97. J. Sun, K.C. Toh, and G.Y Zhao, An analytic center cutting plane method for semidefinite feasibility problems,
Mathematics of Operations Research, 27 (2002), pp. 332–346.
98. K.C. Toh, and M. Kojima, Solving some large scale semidefinite programs via the conjugate residual method,
SIAM J. Optimization, 12 (2002), pp. 669–691.
99. K.C. Toh, A note on the calculation of step-lengths in interior-point methods for semidefinite programming,
Computational Optimization and Applications, 21 (2002), pp. 301–310.
100. K.K. Phoon, K.C. Toh, S.H. Chan, and F.H. Lee An efficient diagonal preconditioner for finite element solution of Biot’s consolidation equations,
International J. Numerical Methods in Engineering, 55 (2002), pp. 377–400.

## 1999–2001

101. A. Ron, Z.W. Shen, and K.C. Toh, Computing the Sobolev regularity of refinable functions by the the Arnoldi Method,
SIAM J. Matrix Analysis and Applications, 23 (2001), pp. 57–76.
102. K.C. Toh, Some new search directions for primal-dual interior point methods in semidefinite programming,
SIAM J. Optimization, 11 (2000), pp. 223–242.
103. K.C. Toh, and L.N. Trefethen, The Kreiss Matrix Theorem on a general complex domain,
SIAM J. Matrix Analysis and Applications, 21 (1999), pp. 145–165.
104. K.C. Toh, M.J. Todd, and R.H. Tutuncu, SDPT3 — a Matlab software package for semidefinite programming,
Optimization Methods and Software, 11 (1999), pp. 545–581.
105. K.C. Toh, Primal-dual path-following algorithms for determinant maximization problems with linear matrix inequalities,
Computational Optimization and Applications, 14 (1999), pp. 309–330.

## 1994–1998

106. T.A. Driscoll, K.C. Toh and L.N. Trefethen, From potential theory to matrix iterations in six steps,
SIAM Review, 40 (1998), pp. 547-578.
107. M.J. Todd, K.C. Toh, and R.H. Tutuncu, On the Nesterov-Todd direction in semidefinite programming,
SIAM J. of Optimization, 8 (1998), pp. 769–796.
108. K.C. Toh and L.N. Trefethen, The Chebyshev Polynomials of a Matrix,
SIAM J. Matrix Analysis and Applications, 20 (1998), pp. 400-419.
109. K.C. Toh, GMRES vs. ideal GMRES,
SIAM J. of Matrix Analysis and Applications, 18 (1997), pp. 30–36.
110. K.C. Toh and L.N. Trefethen, Calculation of pseudospectra by the Arnoldi iteration,
SIAM J. of Scientific Computing, 17 (1996), pp. 1–15.
111. K.C. Toh and L.N. Trefethen, Pseudozeros of polynomials and pseudospectra of companion matrices,
Numerische Mathematik, 68 (1994), pp. 403–425.
112. K.C. Toh and S. Mukherjee, Hypersingular and finite part integrals in the boundary element method,
International J. of Solids and Structures, 31 (1994), pp. 2299–2312.

## Refereed Conference Papers

1. Y.C. Yuan, D.F. Sun, and K.C. Toh, An efficient semismooth Newton based algorithm for convex clustering,
Oral presentation, International Conference on Machine Learning (ICML) 2018. arXiv:1802.07091.
2. Z.W. Li, S.G. Yang, L.-F. Cheong, and K.C. Toh, Simultaneous Clustering and Model Selection for Tensor Affinities,
Spotlight presentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
3. Z.Z. Zhang, G.L. Li, K.C. Toh, and W. Sung, Inference of spatial organizations of chromosomes using semidefinite embedding approach and Hi-C data,
RECOMB 2013, The 17th Annual International Conference on Research in Computational Molecular Biology, Beijing, China, April 7-10, 2013.
In “Research in Computational Molecular Biology”, Lecture Notes in Computer Science, Volume 7821, 2013, Springer, pp. 317–332.
4. Krishna B. Chaudhary, K.K. Phoon, and K.C. Toh, Fast iterative solution of large soil-structure interaction problems in varied ground conditions,
Proceedings of 14th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering, Hong Kong, China, 23-27 May 2011.
5. K. B. Chaudhary, K.K. Phoon, and K.C. Toh, Comparison of MSSOR versus ILU(0) Preconditioners for Biot’s FEM Consolidation Equations,
The 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG), 1-6 October 2008, Goa, India.
6. X. Chen, K.K. Phoon, and K.C. Toh, Symmetric indefinite preconditioners for FE solution of Biot’s consolidation problem,
Geotechnical Engineering in the Information Technology Age (2006): CDROM. Reston: ASCE. (GeoCongress2006, 26 Feb – 1 Mar 2006, Atlanta, United States).
7. K.C. Toh, R.H. Tutuncu, and M.J. Todd, On the implementation of SDPT3 (version 3.1) — a Matlab software package for semidefinite-quadratic-linear programming,
IEEE Conference on Computer-Aided Control System Design, Taipei, Taiwan, 2-4 September 2004.
8. F. Ting, W.J. Heng, and K.C. Toh, Question classification for e-learning by artificial neural network,
Fourth International Conference on Information, Communications & Signal Processing and Fourth IEEE Pacific-Rim Conference On Multimedia, 15-18 December 2003, Singapore.
9. K.K. Phoon, K.C. Toh, S.H. Chan, and F.H. Lee, A generalized Jacobi preconditioner for finite element solution of large-scale consolidation problems,
in Second MIT Conference on Computational Fluid and Solid Mechanics, 17–20 June 2003, Massachusetts Institute of Technology, Cambridge, United States, Vol.1, pp. 573–577, 2003.
10. G.L. Zhou, K.C. Toh, and J. Sun, Efficient algorithms for the smallest enclosing ball problem in high dimensional space,
Novel Approaches to Hard Discrete Optimization, Proceedings of Fields Institute of Mathematics, P. Pardalos and H. Wolkowicz eds., Canadian Mathematical Society, 2002.

## Book chapters and others

1. K.C. Toh, INFORMS OS Today, Volume 8 Number 2 (2008), pp. 7–20.
2. K.C. Toh, M.J. Todd, and R.H. Tutuncu, in Handbook on semidefinite, cone and polynomial optimization: theory, algorithms, software and applications, M. Anjos and J.B. Lasserre eds., Springer, 2012, pp. 715–754.
Here is the complete performance results obtained by SDPT3-4.0 on over 400 problems.
3. X.Y. Fang and K.C. Toh, Using a distributed SDP approach to solve simulated protein molecular conformation problems,
in Distance Geometry: Theory, Methods, and Applications, A. Mucherino, C. Lavor, L. Liberti, and N. Maculan eds., Springer, 2013, pp. 351–376.
4. K.F. Jiang, D.F. Sun, and K.C. Toh,
Fields Institute Communications Volume 69, Discrete Geometry and Optimization, K. Bezdek, Y. Ye, and A. Deza eds., Springer, 2013, pp. 133–162.