We proposed several fast Semidefinite Programming (SDP) algorithms are proposed to solve large-scale binary quadratic problems in vision applications.
Related work:
√ P. Wang, C. Shen, A. van den Hengel, P. H. S. Torr. Large-scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications. In: TPAMI, 2017.
√ P. Wang, C. Shen, A. van den Hengel, P. H. S. Torr. Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference. In: IJCV, 2016.
√ P. Wang, C. Shen, A. van den Hengel. Efficient SDP Inference for Fully-connected CRFs based on Low-rank Decomposition. In: CVPR, 2015.
√ P. Wang, C. Shen, A. van den Hengel. A Fast Semidefinite Approach to Solving Binary Quadratic Problems. In: CVPR, 2013.