Ji-Ming Peng - Citegraph

Author Info

Name	Affiliation	Papers
JI-MING PENG	State Key Laboratory of Scientific and Engineering Computing, Institute of Computational Mathematics and Scientific Computing, Academic Sinica, Beijing, P.O. Box 2719, China, 100080, Current address: Faculty of Technical Mathematics and Informatics, Delft University of Technology, P.O. Box 5031, 2628 CD, Mekelweg 4, Delft, The Netherlands, e-mail: j.peng@its.tudelft.nl CN	43
Collaborators	Citations	PageRank
55	500	45.74
Referers	Referees	References
816	748	532

Search Limit

100816

Publications (43 rows)

Collaborators (55 rows)

Referers (100 rows)

Referees (100 rows)

Title	Citations	PageRank	Year
Complexity Results And Effective Algorithms For Worst-Case Linear Optimization Under Uncertainties	0	0.34	2021
Preface: special issue of MOA 2018.	0	0.34	2020
Enhancing Semidefinite Relaxation for Quadratically Constrained Quadratic Programming via Penalty Methods	0	0.34	2019
Vulnerability Analysis of the Financial Network	0	0.34	2019
New global algorithms for quadratic programming with a few negative eigenvalues based on alternative direction method and convex relaxation	0	0.34	2019
Preface: Special issue of MOA 2016.	2	0.37	2018
Efficiently solving total least squares with Tikhonov identical regularization.	1	0.37	2018
A Lagrangian search method for the P-median problem	0	0.34	2017
Network Flow Formulations For Learning Binary Hashing	0	0.34	2016
Semi-definite programming relaxation of quadratic assignment problems based on nonredundant matrix splitting	5	0.44	2015
A nonlinear semidefinite optimization relaxation for the worst-case linear optimization under uncertainties.	3	0.37	2015
New Analysis on Sparse Solutions to Random Standard Quadratic Optimization Problems and Extensions	3	0.53	2015
Sparse solutions to random standard quadratic optimization problems.	8	0.66	2013
An efficient algorithm for maximal margin clustering	7	0.48	2012
Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging.	6	0.43	2012
Learning Kernels For Variants Of Normalized Cuts: Convex Relaxations And Applications	5	0.62	2010
A new relaxation framework for quadratic assignment problems based on matrix splitting	11	0.59	2010
Ensemble Clustering using Semidefinite Programming with Applications.	30	1.23	2010
Estimating Bounds for Quadratic Assignment Problems Associated with Hamming and Manhattan Distance Matrices Based on Semidefinite Programming	10	0.55	2010
Optimization-based dynamic sensor management for distributed multitarget tracking	23	1.01	2009
Self-adaptive support vector machines: modelling and experiments	4	0.47	2009
Generalized median graphs and applications	17	0.74	2009
A confidence voting process for ranking problems based on support vector machines	6	0.47	2009
PREFACESpecial section on mathematical programming in data mining and machine learning.	0	0.34	2008
Generalized Median Graphs: Theory and Applications	7	0.62	2007
Approximating K-means-type Clustering via Semidefinite Programming	42	1.52	2007
Exact Penalty Functions for Constrained Minimization Problems via Regularized Gap Function for Variational Inequalities	4	0.49	2007
Ensemble Clustering using Semidefinite Programming.	17	1.04	2007
Refining Spherical K-Means For Clustering Documents	1	0.36	2006
On approximate balanced bi-clustering	1	0.35	2005
A Cutting Algorithm for the Minimum Sum-of-Squared Error Clustering	8	0.51	2005
A Predictor-Corrector Algorithm for Linear Optimization Based on a Specific Self-Regular Proximity Function	14	1.12	2005
A continuation method for the linear second-order cone complementarity problem	3	0.43	2005
A new class of polynomial primal-dual methods for linear and semidefinite optimization	18	2.44	2002
Primal-Dual Interior-Point Methods for Second-Order Conic Optimization Based on Self-Regular Proximities	20	1.88	2002
Self-regular functions and new search directions for linear and semidefinite optimization	62	4.60	2002
A dynamic large-update primal-dual interior-point method for linear optimization.	2	0.44	2002
A Strongly Polynomial Rounding Procedure Yielding a Maximally Complementary Solution for $P_*(\kappa)$ Linear Complementarity Problems	19	1.24	2000
New Complexity Analysis of the Primal-Dual Newton Method for Linear Optimization	16	2.55	2000
A non-interior continuation method for generalized linear complementarity problems	30	2.57	1999
A hybrid Newton method for solving the variational inequality problem via the D-gap function	29	1.97	1999
Optimality Conditions for the Minimization of a Quadratic with Two Quadratic Constraints	43	7.29	1997
Equivalence of variational inequality problems to unconstrained minimization	23	2.26	1997