So far, Peng has published a research monograph, and over sixty papers in  leading optimization journals and various CS/IEEE conference proceedings. Below is some selected publications:


  • J. Peng, C. Roos and T. Terlaky, Self-Regularity: A New Paradigm for primal-Dual Interior-Point Methods. Princeton University Press, April, 2002.

Publications in major optimization journals

  • John R. Birge, Aein Khabazian, Jiming Peng. Optimization Modeling and Techniques for Systemic Risk Assessment and Control in Financial Networks.
    INFORMS TutORials in Operations Research: Recent Advances in Optimization and Modeling of Contemporary Problems. Informs Annual Meeting, October, 2018.
  • Hezhi Luo,Xiaodi Bai, Gino Lim and Jiming Peng. Global Algorithms for Quadratic Programming with A Few Negative Eigenvalues Based on Successive Linear Optimization and Convex Relaxation. Mathematical Programming: Computation, 2018. [doi]
    • Aein Khabazian and Jiming Peng. Vulnerability Analysis of the Financial Network. Management Science, 2019. [doi]
    • J. Chen, L.M. Feng and J. Peng. Optimal deleveraging with nonlinear temporary price impact. E. J. Operations Research, 244(1), 240–247, 2015.
    • J. Peng, T. Zhu, H. Luo and K. Toh. Semidefinite programming relaxatios of quadratic assignment problems based on nonredundant matrix splitting. J. Comp. Optimi. Applications, 60(1), 171-198, 2015.
    • X. Chen and J. Peng. A new analysis on sparse solutions to random standard quadratic programming problems and extensions.  Mathematics of Operations Research,40(3):725-738, 2015.
    • J. Peng and T. Zhu. A Nonlinear Semidefinite Optimization Relaxation for the Worst-case Linear Optimization under Uncertainties.  Mathematical Programming, 152(1), 593-614, 2015.
    • J. Chen, L. Feng, J. Peng and Y. Ye. Analytic results and effective algorithm for optimal portfolio liquidation with market impact.  Operations Research, 62(1), 195-206, 2014.
    • P. Jiang, J. Peng, M. Heath and R. Yang. CBMF: a clustering approach for binary matrix factorization. Data Mining and Knowl. Discov. for Big Data: Methodologies, Challenges, and Opportunities, Eds W. Chu., Springer series “Studies in Big Data”,  281-303, 2013.
    • X. Chen, J. Peng and Sh. Zhang. Sparse solutions to random standard quadratic programming problems. Mathematical Programming, Vol 141(1),273-293, 2013.
    • V. Singh, L. Mukherjee, J. Peng and J. Xu. Ensemble Clustering using Semidefinite Programming with applications. Machine Learning , Vol 79 (1-2), 177-200, 2010.
    • H. Mittleman, J. Peng, Estimating bounds for quadratic assignment problems associated with Hamming and Manhattan distance matrices based on semidefinite programming. SIAM J. Optim. Volume 20, Issue 6, pp. 3408-3426, 2010.
    • J. Peng, H. Mittleman and X. Li, A new convex relaxation framework for quadratic assignment problems based on matrix splitting. Math. Programming: Computation, 1(2), 59-77, 2010.
    • M. Salahi, J. Peng and T. Terlaky, On Mehrotra-type predictor-corrector algorithms. SIAM J. Optim. 18 (2007), no. 4, 1377–1397.
    • J. Peng and Y. Wei, Approximating K-means-type clustering via semidefinite programming. SIAM J. Optimization. 18 (2007), no. 1, 186–205.
    • 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, V(15), 1105–1127, 2005.
    • J. Peng, C. Roos and T. Terlaky, Self-regular functions and new search directions for linear and semidefinite optimization. Math. Programming, v(93), 129-171, 2002.
    • J. Peng, C. Roos and T. Terlaky, Primal-Dual Interior-Point Methods for Second-Order Conic Optimization Based on Self-Regular Proximities. SIAM J. Optimization, V(13), 179-202,2002.
    • E. Klerk, J. Peng, C. Roos and T. Terlaky, A Scaled Gauss-Newton Primal-Dual Search Direction for SDP, SIAM J. Optimization, V(11), 870-888,2001.
    • 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, V(11), 320-340,2000.
    • J.Peng and M. Fukushima, A hybrid Newton method for solving variational inequalities via the D-gap functions, Math. Programming, V(86), 367-386, 1999.
    • J. Peng and Z. Lin, A Non-interior Continuation Method for Generalized Linear Complementarity Problems, Math. Programming, V(86), 533-563, 1999.
    • J. Peng and Y. Yuan, Optimality conditions for the minimization of a quadratic with two quadratic constraints. SIAM. J. Optimization 7(1997), 579-594.
    • J. Peng, Equivalence of Variational Inequality problems to Unconstrained Minimization. Math. Prog., 78(1997),347-355.

    Other recent publications

    • R. Tharmarasa, T. Kirubarajan , J. Peng and T. Lang. Optimization-based dynamic sensor management for distributed multitarget tracking. IEEE Transactions on Systems, Man, and Cybernetics–Part C: Applications and Reviews, 5(39), 534-546, 2009.
    • T. Jiao, J. Peng and T. Terlaky. A confidence voting process for ranking problems based on support vector machines, Ann Oper. Res., 166, 23-38, 2009.
    • L. Mukherjee, J. Peng, V. Singh, D. Schuurmanns and J. Xu, An efficient algorithm for Maximal Margin Clustering. J. Global Optimization, 52(1), 123–137, 2012.
    • L. Mukherjee, V. Singh, J. Peng and C. Hinrichs. Learning Kernels for variants of Normalized Cuts: Convex Relaxations and Applications, Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), June 2010. (acceptance 27%).
    • V. Singh, L. Mukherjee, J. Peng, J. Xu, Ensemble Clustering using Semidefinite Programming, Proceedings of Advances in Neural Information Processing Systems (NIPS), December, 2007.
    • L. Mukherjee, V. Singh, J. Peng, J. Xu, M.J. Zeitz, R. Berezney, Generalized Median Graphs: Theory and Applications, Proceedings of IEEE International Conference on Computer Vision (IEEE ICCV), October, 2007.
    • W. Li and J. Peng, Exact penalty functions for constrained minimization problems via regularized gap function for variational inequalities. J. Global Optim. 37 (2007), no. 1, 85–94.
    • H.R. Chen and J. Peng, 0-1 Semidefinite Programming for Graph-Cut Clustering: Modelling and Approximation, invited paper for CRM Proceedings & Lecture Notes of the American Mathematical Society.

    Technical Reports

    • Aein Khabazian and Jiming Peng. Vulnerability Analysis of the Financial Network. 40 Pages. Under review for Management Science. [pdf]
    • J. Chen, L. Feng and J. Peng. Optimal portfolio liquidation with a Markov chain approximation approach. Submitted, 2014.
    • J. Chen, L. Feng and J. Peng. Robust portfolio deleveraging with margin requirement. Submitted, 2014.
    • J. Hale, E. Zhou and J. Peng. A Lagrangian Search Method for the K-Median Problem. Submitted, 2014.