SDPT3—a MATLAB software package for semidefinite programming, version 1.3 KC Toh, MJ Todd, RH Tütüncü Optimization Methods and Software 11 (1-4), 545-581, 1999 | 2399 | 1999 |

Solving semidefinite-quadratic-linear programs using SDPT3 RH Tütüncü, KC Toh, MJ Todd Mathematical programming 95 (2), 189-217, 2003 | 1462 | 2003 |

An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems KC Toh, S Yun Pacific Journal of Optimization 6, 615--640, 2010 | 1194 | 2010 |

Semidefinite programming approaches for sensor network localization with noisy distance measurements P Biswas, TC Liang, KC Toh, Y Ye, TC Wang IEEE transactions on automation science and engineering 3 (4), 360-371, 2006 | 537 | 2006 |

On the Nesterov--Todd Direction in Semidefinite Programming MJ Todd, KC Toh, RH Tütüncü SIAM Journal on Optimization 8 (3), 769-796, 1998 | 418 | 1998 |

A Newton-CG augmented Lagrangian method for semidefinite programming XY Zhao, D Sun, KC Toh SIAM J. Optimization 20, 1737--1765, 2010 | 392 | 2010 |

On the implementation and usage of SDPT3–a Matlab software package for semidefinite-quadratic-linear programming, version 4.0 KC Toh, MJ Todd, RH Tütüncü Handbook on Semidefinite, Conic and Polynomial Optimization, 715-754, 2012 | 207 | 2012 |

SDPNAL: a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints L Yang, D Sun, KC Toh Mathematical Programming Computation 7 (3), 331-366, 2015 | 203 | 2015 |

A convergent 3-block semiproximal alternating direction method of multipliers for conic programming with 4-type constraints D Sun, KC Toh, L Yang SIAM journal on Optimization 25 (2), 882-915, 2015 | 152* | 2015 |

From potential theory to matrix iterations in six steps TA Driscoll, KC Toh, LN Trefethen SIAM review, 547-578, 1998 | 144 | 1998 |

Pseudozeros of polynomials and pseudospectra of companion matrices KC Toh, LN Trefethen Numerische Mathematik 68 (3), 403-425, 1994 | 144 | 1994 |

An inexact interior point method for L 1-regularized sparse covariance selection. L Li, KC Toh Math. Program. Comput. 2 (3-4), 291-315, 2010 | 142 | 2010 |

An implementable proximal point algorithmic framework for nuclear norm minimization YJ Liu, D Sun, KC Toh Mathematical Programming 133, 399--436, 2012 | 138 | 2012 |

An efficient inexact symmetric Gauss–Seidel based majorized ADMM for high-dimensional convex composite conic programming L Chen, D Sun, KC Toh Mathematical Programming 161, 237-270, 2017 | 136 | 2017 |

3D chromosome modeling with semi-definite programming and Hi-C data ZZ Zhang, G Li, KC Toh, WK Sung Journal of computational biology 20 (11), 831-846, 2013 | 135 | 2013 |

A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions X Li, D Sun, KC Toh Mathematical Programming 155 (1-2), 333-373, 2016 | 131 | 2016 |

A distributed SDP approach for large-scale noisy anchor-free graph realization with applications to molecular conformation P Biswas, KC Toh, Y Ye SIAM Journal on Scientific Computing 30 (3), 1251--1277, 2008 | 131 | 2008 |

A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems X Li, D Sun, KC Toh SIAM Journal on Optimization 28 (1), 433-458, 2018 | 130 | 2018 |

A coordinate gradient descent method for l1-regularized convex minimization S Yun, KC Toh Computational Optimization and Applications 48 (2), 273-307, 2011 | 129 | 2011 |

An inexact primal–dual path following algorithm for convex quadratic SDP KC Toh Mathematical programming 112 (1), 221-254, 2008 | 123 | 2008 |