 Jiming
Peng
Assistant Professor
Jiming Peng received a B.S. degree in computational mathematics and
software from Xiangtan University (1987, China), an M.S. degree in
computational mathematics from Chinese Academy of Science (1993), and
PhD in information
technology and system from Delft University
of Technology, the Netherlands (2001). From 2001 to 2006, he worked
in the department of computing and software at McMaster University
as an assistant professor, where was also an associate member of
the department
of mathematics and statistics at McMaster. His research interest
covers several different areas within the field of mathematical programming
including numerical methods for variational inequalities and complementary
problems, interior-point methods for linear conic optimization, optimization
modeling and problem solving
in knowledge discovery, decision making and engineering design.
Awards
The Stieltjes Prize from the Netherlands, 2003.
The Premier's Research Excellence Award from Ontario, 2003. Finalist
of the A.W. Tucker prize awarded by the mathematical programming
society, 2003.
Contact Information
Office: 216d Transportation Building
Mailing Address:
117 Transportation Building MC-238
104 S. Mathews Ave.
Urbana IL 61801
Phone: (217) 244-6275
Fax: (217) 244-5705
e-mail: pengj@mcmaster.ca
Representative
Publications
J. Peng, Equivalence of Variational Inequality problems
to Unconstrained Minimization. Mathematical Programming,
78(1997),347-355.
J. Peng, Z.
Lin, A Non-interior Continuation Method for Generalized
Linear Complementarity Problems, Mathematical Programming,
Vol(86), 533-563, 1999.
T. Illes,
J. Peng, C. Roos and T. Terlaky, A strongly polynomial
rounding precedure yielding a maximally complementarity solution
for P*(k) linear complementarity problems SIAM J. Optimization,
Vol(11), 320-340,2000.
J. Peng, C.
Roos and T. Terlaky, Self-Regularity: A New Paradigm
for primal-Dual Interior-Point Methods.
Princeton University Press, April, 2002.
J. Peng, C. Roos
and T. Terlaky, Self-regular functions and new search
directions for linear and
semidefinite optimization. Mathematical Programming, vol(93),
129--171, 2002.
J. Peng,
T. Terlaky and Y. Zhao, A predictor-corrector algorithm
for linear optimization based on a specific self regular proximity
function. SIAM J. Optimization, 2005.
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