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 | 2503 | 1999 |

Solving semidefinite-quadratic-linear programs using SDPT3 RH Tütüncü, KC Toh, MJ Todd Mathematical programming 95 (2), 189-217, 2003 | 1522 | 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 | 1272 | 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 | 558 | 2006 |

A Newton-CG augmented Lagrangian method for semidefinite programming XY Zhao, D Sun, KC Toh SIAM J. Optimization 20, 1737--1765, 2010 | 426 | 2010 |

On the Nesterov--Todd direction in semidefinite programming MJ Todd, KC Toh, RH Tütüncü SIAM Journal on Optimization 8 (3), 769-796, 1998 | 426 | 1998 |

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 | 228 | 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 | 227 | 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 | 165* | 2015 |

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 | 156 | 2018 |

An inexact interior point method for *L* _{1}-regularized sparse covariance selectionL Li, KC Toh Mathematical Programming Computation 2, 291-315, 2010 | 152 | 2010 |

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 | 150 | 2017 |

From potential theory to matrix iterations in six steps TA Driscoll, KC Toh, LN Trefethen SIAM review, 547-578, 1998 | 149 | 1998 |

Pseudozeros of polynomials and pseudospectra of companion matrices KC Toh, LN Trefethen Numerische Mathematik 68 (3), 403-425, 1994 | 145 | 1994 |

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 | 144 | 2016 |

An implementable proximal point algorithmic framework for nuclear norm minimization YJ Liu, D Sun, KC Toh Mathematical Programming 133, 399--436, 2012 | 142 | 2012 |

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 | 141 | 2013 |

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 | 137 | 2008 |

A coordinate gradient descent method for *ℓ* _{1}-regularized convex minimizationS Yun, KC Toh Computational Optimization and Applications 48, 273-307, 2011 | 135 | 2011 |

Solving log-determinant optimization problems by a Newton-CG primal proximal point algorithm C Wang, D Sun, KC Toh SIAM Journal on Optimization 20 (6), 2994--3013, 2010 | 127 | 2010 |