Self-regularity J Peng, C Roos, T Terlaky Self-Regularity, 2009 | 356 | 2009 |
Self-regular functions and new search directions for linear and semidefinite optimization J Peng, C Roos, T Terlaky Mathematical Programming 93 (1), 129-171, 2002 | 263 | 2002 |
Approximating k-means-type clustering via semidefinite programming J Peng, Y Wei SIAM journal on optimization 18 (1), 186-205, 2007 | 202 | 2007 |
Equivalence of variational inequality problems to unconstrained minimization JM Peng Mathematical Programming 78 (3), 347-355, 1997 | 145 | 1997 |
Scale invariant cosegmentation for image groups L Mukherjee, V Singh, J Peng CVPR 2011, 1881-1888, 2011 | 141 | 2011 |
Optimal nearly analytic discrete approximation to the scalar wave equation D Yang, J Peng, M Lu, T Terlaky Bulletin of the Seismological Society of America 96 (3), 1114-1130, 2006 | 125 | 2006 |
Optimality conditions for the minimization of a quadratic with two quadratic constraints JM Peng, YX Yuan SIAM Journal on Optimization 7 (3), 579-594, 1997 | 120 | 1997 |
On Mehrotra-type predictor-corrector algorithms M Salahi, J Peng, T Terlaky SIAM Journal on Optimization 18 (4), 1377-1397, 2008 | 97 | 2008 |
A non-interior continuation method for generalized linear complementarity problems JM Peng, Z Lin Mathematical Programming 86 (3), 533-563, 1999 | 96 | 1999 |
A new and efficient large-update interior-point method for linear optimization J Peng, C Roos, T Terlaky ¬£¬í¬é¬Ú¬ã¬Ý¬Ú¬ä¬Ö¬Ý¬î¬ß¬í¬Ö ¬ä¬Ö¬ç¬ß¬à¬Ý¬à¬Ô¬Ú¬Ú 6 (4), 2001 | 90 | 2001 |
An optimal nearly analytic discrete method for 2D acoustic and elastic wave equations D Yang, M Lu, R Wu, J Peng Bulletin of the Seismological Society of America 94 (5), 1982-1992, 2004 | 84 | 2004 |
Primal-dual interior-point methods for second-order conic optimization based on self-regular proximities J Peng, C Roos, T Terlaky SIAM Journal on Optimization 13 (1), 179-203, 2002 | 77 | 2002 |
Optimization-based dynamic sensor management for distributed multitarget tracking R Tharmarasa, T Kirubarajan, J Peng, T Lang IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and ¡¦, 2009 | 75 | 2009 |
Ensemble clustering using semidefinite programming with applications V Singh, L Mukherjee, J Peng, J Xu Machine learning 79 (1), 177-200, 2010 | 73 | 2010 |
A hybrid Newton method for solving the variational inequality problem via the D-gap function JM Peng, M Fukushima Mathematical Programming 86 (2), 367-386, 1999 | 73 | 1999 |
A new class of polynomial primal–dual methods for linear and semidefinite optimization J Peng, C Roos, T Terlaky European Journal of Operational Research 143 (2), 234-256, 2002 | 67 | 2002 |
A new theoretical framework for k-means-type clustering J Peng, Y Xia Foundations and advances in data mining, 79-96, 2005 | 63 | 2005 |
A simply constrained optimization reformulation of KKT systems arising from variational inequalities F Facchinei, A Fischer, C Kanzow, J M Peng Applied Mathematics and Optimization 40 (1), 19-37, 1999 | 61 | 1999 |
New complexity analysis of the primal—dual newton method for linear optimization J Peng, C Roos, T Terlaky Annals of operations research 99 (1), 23-39, 2000 | 56 | 2000 |
A Strongly Polynomial Rounding Procedure Yielding a Maximally Complementary Solution for P_*(¥ê) Linear Complementarity Problems T Illés, J Peng, C Roos, T Terlaky SIAM Journal on Optimization 11 (2), 320-340, 2000 | 51 | 2000 |