@article{lu_deng_fang_jin_xing_2019, title={Fast computation of global solutions to the single-period unit commitment problem}, volume={44}, ISSN={1382-6905 1573-2886}, url={http://dx.doi.org/10.1007/s10878-019-00489-9}, DOI={10.1007/s10878-019-00489-9}, number={3}, journal={Journal of Combinatorial Optimization}, publisher={Springer Science and Business Media LLC}, author={Lu, Cheng and Deng, Zhibin and Fang, Shu-Cherng and Jin, Qingwei and Xing, Wenxun}, year={2019}, month={Nov}, pages={1511–1536} }
 @article{jin_yu_lavery_fang_2012, title={Univariate cubic L-1 interpolating splines based on the first derivative and on 5-point windows: analysis, algorithm and shape-preserving properties}, volume={51}, ISSN={["1573-2894"]}, DOI={10.1007/s10589-011-9426-y}, number={2}, journal={COMPUTATIONAL OPTIMIZATION AND APPLICATIONS}, author={Jin, Qingwei and Yu, Lu and Lavery, John E. and Fang, Shu-Cherng}, year={2012}, month={Mar}, pages={575–600} }
 @article{lu_fang_jin_wang_xing_2011, title={KKT SOLUTION AND CONIC RELAXATION FOR SOLVING QUADRATICALLY CONSTRAINED QUADRATIC PROGRAMMING PROBLEMS}, volume={21}, ISSN={["1095-7189"]}, DOI={10.1137/100793955}, abstractNote={To find a global optimal solution to the quadratically constrained quadratic programming problem, we explore the relationship between its Lagrangian multipliers and related linear conic programming problems. This study leads to a global optimality condition that is more general than the known positive semidefiniteness condition in the literature. Moreover, we propose a computational scheme that provides clues of designing effective algorithms for more solvable quadratically constrained quadratic programming problems.}, number={4}, journal={SIAM JOURNAL ON OPTIMIZATION}, author={Lu, Cheng and Fang, Shu-Cherng and Jin, Qingwei and Wang, Zhenbo and Xing, Wenxun}, year={2011}, pages={1475–1490} }
 @article{jin_fang_xing_2010, title={On the global optimality of generalized trust region subproblems}, volume={59}, ISSN={["0233-1934"]}, DOI={10.1080/02331930902995236}, abstractNote={Quadratically constrained quadratic programming is an important class of optimization problems. We consider the case with one quadratic constraint. Since both the objective function and its constraint can be neither convex nor concave, it is also known as the ‘generalized trust region subproblem.’ The theory and algorithms for this problem have been well studied under the Slater condition. In this article, we analyse the duality property between the primal problem and its Lagrangian dual problem, and discuss the attainability of the optimal primal solution without the Slater condition. The relations between the Lagrangian dual and semidefinite programming dual is also given.}, number={8}, journal={OPTIMIZATION}, author={Jin, Qingwei and Fang, Shu-Cherng and Xing, Wenxun}, year={2010}, pages={1139–1151} }