BUSB 5021
Yu Du’s research interest is in large scale data-driven mathematical optimization. The majority of her research focuses on developing nonlinear optimization algorithms for solving large-scale problems with applications in machine learning and data mining, specifically statistical convex and non-convex optimization problems.
Du has also been working on modeling and solving combinatorial optimization problems. Much of her efforts are concerned with new ways of modeling quadratic binary problems with applications in quantum computing and with designing and testing new algorithms for solving these problems.
Education
PhD Operations Research, RUTCOR, Rutgers University
MS Quantitative Finance, Rutgers University
BS Economics, Central South University
Areas of expertise
Nonlinear Optimization, Combinatorial Optimization, Machine Learning, Quantum Computing and Business Analytics
Publications and presentations
Du, Y., Lin, X. and Ruszczynski, A., A Selective Linearization (SLIN) Method for Multi-block Convex Optimization, SIAM Journal on Optimization 27 (2017), 1102–1117 (DOI: 10.1137/15M103217X.)
Du, Y. and Ruszczynski, A., Rate of Convergence of the Bundle Method, Journal of Optimization Theory and Applications 173 (2017), 908–922 (DOI: 10.1007/s10957-017-1108-1.)
F. Glover, G. Kochenberger, Y. Du, A Tutorial on Formulating and Using QUBO Models, https://arxiv.org/abs/1811.11538 , 2019.
Glover, F., Kochenberger, and Du, Y., Quantum Bridge Analytics I: A Tutorial on Formulating and Using QUBO Models, 4OR, vol. 17(4), pages 335-371, 2019.
Glover, F., Kochenberger, G., Ma, M. and Du, Y., Quantum Bridge Analytics II: QUBO-Plus, Network Optimization and Combinatorial Chaining for Asset Exchange, 4OR, vol. 18, pages 387-417, Springer, 2020, (DOI: 10.1007/s10288-020-00464-9).
Du, Y., Lin, X. and Ruszczynski, A., Selective Linearization for Multi-block Statistical Learning, European Journal of Operational Research, 293 (2020), 1, 219–228, Elsevier, (DOI:10.1016/S03772- 217-20310-201), 2020.
Pham, M., Du, Y., Lin, X., and Ruszczynski, A., An outer-inner linearization method for nonconvex and nondifferentiable composite regularization problems, Journal of Global Optimization(2021),81, pages 179–202.
Du, Y., Glover, F.,Kochenberger, G., Lewis, M and Wang, H., Solving Clique Partitioning Problems: A Comparison of Models and Commercial Solvers, Int. J. of Information Technology and Decision Making, 21, 1, pp.59–81, 2021.
Glover, F., Kochenberger, Hennig, R., and Du, Y., Quantum Bridge Analytics I: A Tutorial on Formulating and Using QUBO Models (An extended version) Annuals of Operations Research 314(2022), 141–183. https://doi.org/10.1007/s10479-022-04634-2
Glover, F., Kochenberger, G., Ma, M. and Du, Y., Quantum Bridge Analytics II: QUBO-Plus, Network Optimization and Combinatorial Chaining for Asset Exchange (An extended version) Annuals of Operations Research 314(2022), 185–212. https://doi.org/10.1007/s10479-022-04695-3
Du, Y., He, J., Liu, M., Su, Y., Reciprocity in College Teaching: A Big Data Study Based on Online Student Evaluation of 919,750 Professors, Assessment and Evaluation in Higher Education (2022) https://doi.org/10.1080/02602938.2022.2067980
Glover, F., Kochenberger, and Du, Y., “Applications and Computational Advances for Solving the QUBO Model,” in The quadratic unconstrained binary optimization problem: theory, algorithms and applications , A. P. Punnen (editor), Springer, 2022.
Y. Du, F. Glover, G. Kochenberger, Metaheuristics vs. exact solvers: finding optimal solutions to the minimum sum coloring problem, EURO 2022, Finland, 2022.
M. Pham, Y. Du, X. Lin, A. Ruszczynski, An outer–inner linearization method for nonconvex and nondifferentiable composite regularization problems, ICCOPT 2019, Berlin.
Y. Du, F. Glover, G. Kochenberger, H. Wang, An Improved Study on the Quadratic Knapsack Problem with Multiple Constraints, EURO 2019, Dublin, 2019.
Y. Du, F. Glover, G. Kochenberger, H. Wang, Solving Weighted Vertex Covering Problems: A Comparison of Models and Commercial Solvers., EURO 2019, Dublin, 2019.
Y. Du, X. Lin, A. Ruszczynski, Selective linearization for statistical learning, the 23rd International Symposium on Mathematical Programming, July 2018, Bordeaux, France.
Y. Du, X. Lin, A. Ruszczynski, Selective linearization method for statistical learning, contributed seminar at DIMACS, Rutgers University, June 2018, Piscataway, NJ.
https://www.youtube.com/watch?v=4vqyDu99YE4&list=PLKVCRT3MRed4pvskhC7ciyGa6xLD_Wz0g&index=21&t=517s
Awards
Outstanding Research Award – University of Colorado Denver Business School, 2022
Faculty Research Productivity Award – University of Colorado Denver Business School, 2022
Summer 2021 CIBER Faculty Grant – University of Colorado Denver Business School, 2021
Faculty Research Award – University of Colorado Denver Business School, 2021
Excellence Fellowship in Department of Operations Research, Rutgers University, 2012
Distinguished Graduates, 2010
National Scholarship of Ministry of Education of People’s Republic of China, 2008-2010
Reviewer: Mathematical Programming. Operations Research. SIAM Journal on Optimization. Journal of Optimization Theory and Applications. European Journal of Operational Research. Annals of Operations Research, Journal of Global Optimization, Frontiers in Applied Mathematics and Statistics Section Optimization, Discrete Applied Mathematics, Computational Optimization and Applications, Journal of Scheduling, etc.
Professional Memberships: The Mathematical Programming Society, Institute for Operations Research and the Management Sciences (INFORMS), and Society for Industrial and Applied Mathematics (SIAM). Association of European Operational Research Societies (EURO)
