@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{guo_deng_fang_xing_2014, title={QUADRATIC OPTIMIZATION OVER ONE FIRST-ORDER CONE}, volume={10}, ISSN={["1553-166X"]}, DOI={10.3934/jimo.2014.10.945}, abstractNote={This paper studies the first-order cone constrained homogeneous quadratic programming problem. 
 
For efficient computation, the problem is reformulated as a linear conic programming problem. A union of second-order cones are designed to cover the first-order cone such that a sequence of linear conic programming problems can be constructed to approximate the conic reformulation. 
 
Since the cone of nonnegative quadratic forms over a union of second-order cones has a linear matrix inequalities representation, each linear conic programming problem in the sequence is polynomial-time solvable by applying semidefinite programming techniques. The convergence of the sequence is guaranteed when the union of second-order cones gets close enough to the first-order cone. In order to further improve the efficiency, an adaptive scheme is adopted. Numerical experiments are provided to illustrate the efficiency of the proposed approach.}, number={3}, journal={JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION}, author={Guo, Xiaoling and Deng, Zhibin and Fang, Shu-Cherng and Xing, Wenxun}, year={2014}, month={Jul}, pages={945–963} }
 @article{deng_bai_fang_tian_xing_2013, title={A branch-and-cut approach to portfolio selection with marginal risk control in a linear conic programming framework}, volume={22}, ISSN={["1861-9576"]}, DOI={10.1007/s11518-013-5234-5}, number={4}, journal={JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING}, author={Deng, Zhibin and Bai, Yanqin and Fang, Shu-Cherng and Tian, Ye and Xing, Wenxun}, year={2013}, month={Dec}, pages={385–400} }
 @inproceedings{luo_deng_bulatov_lavery_fang_2013, series={Proceedings of SPIE}, title={Comparison of an ℓ1-regression-based and a RANSAC-based planar segmentation procedure for urban terrain data with many outliers}, volume={8892}, ISSN={["1996-756X"]}, url={http://dx.doi.org/10.1117/12.2028627}, DOI={10.1117/12.2028627}, abstractNote={For urban terrain data with many outliers, we compare an ℓ1-regression-based and a RANSAC-based planar segmentation procedure. The procedure consists of 1) calculating the normal at each of the points using ℓ1 regression or RANSAC, 2) clustering the normals thus generated using DBSCAN or fuzzy c-means, 3) within each cluster, identifying segments (roofs, walls, ground) by DBSCAN-based-subclustering of the 3D points that correspond to each cluster of normals and 4) fitting the subclusters by the same method as that used in Step 1 (ℓ1 regression or RANSAC). Domain decomposition is used to handle data sets that are too large for processing as a whole. Computational results for a point cloud of a building complex in Bonnland, Germany obtained from a depth map of seven UAV-images are presented. The ℓ1-regression-based procedure is slightly over 25% faster than the RANSAC-based procedure and produces better dominant roof segments. However, the roof polygonalizations and cutlines based on these dominant segments are roughly equal in accuracy for the two procedures. For a set of artificial data, ℓ1 regression is much more accurate and much faster than RANSAC. We outline the complete building reconstruction procedure into which the ℓ1-regression-based and RANSAC-based segmentation procedures will be integrated in the future.}, booktitle={Image and Signal Processing for Remote Sensing XIX}, publisher={SPIE}, author={Luo, Jian and Deng, Zhibin and Bulatov, Dimitri and Lavery, John E. and Fang, Shu-Cherng}, editor={Bruzzone, LorenzoEditor}, year={2013}, month={Oct}, pages={889209}, collection={Proceedings of SPIE} }
 @article{tian_fang_deng_xing_2013, title={Computable representation of the cone of nonnegative quadratic forms over a general second-order cone and its application to completely positive programming}, volume={9}, number={3}, journal={Journal of Industrial and Management Optimization}, author={Tian, Y. and Fang, S. C. and Deng, Z. B. and Xing, W. X.}, year={2013}, pages={703–721} }
 @article{deng_fang_jin_xing_2013, title={Detecting copositivity of a symmetric matrix by an adaptive ellipsoid-based approximation scheme}, volume={229}, ISSN={0377-2217}, url={http://dx.doi.org/10.1016/j.ejor.2013.02.031}, DOI={10.1016/j.ejor.2013.02.031}, abstractNote={It is co-NP-complete to decide whether a given matrix is copositive or not. In this paper, this decision problem is transformed into a quadratic programming problem, which can be approximated by solving a sequence of linear conic programming problems defined on the dual cone of the cone of nonnegative quadratic functions over the union of a collection of ellipsoids. Using linear matrix inequalities (LMI) representations, each corresponding problem in the sequence can be solved via semidefinite programming. In order to speed up the convergence of the approximation sequence and to relieve the computational effort of solving linear conic programming problems, an adaptive approximation scheme is adopted to refine the union of ellipsoids. The lower and upper bounds of the transformed quadratic programming problem are used to determine the copositivity of the given matrix.}, number={1}, journal={European Journal of Operational Research}, publisher={Elsevier BV}, author={Deng, Zhibin and Fang, Shu-Cherng and Jin, Qingwei and Xing, Wenxun}, year={2013}, month={Aug}, pages={21–28} }