Dual–primal proximal point algorithms for extended convex programming

This paper presents a class of dual–primal proximal point algorithms (PPAs) for extended convex programming with linear constraints. By choosing appropriate proximal regularization matrices, the application of the general PPA to the equivalent variational inequality of the extended convex programming with linear constraints can result in easy proximal subproblems. In theory, the sequence generated by the general PPA may fail to converge since the proximal regularization matrix is asymmetric sometimes. So we construct descent directions derived from the solution obtained by the general PPA. Different step lengths and descent directions are chosen with the negligible additional computational load. The global convergence of the new algorithms is proved easily based on the fact that the sequences generated are Fejér monotone. Furthermore, we provide a simple proof for the O(1/t) convergence rate of these algorithms.     

Redundancy filtering and fusion verification for video copy detection

Multimedia Systems - Recently, the frame fusion based video copy detection scheme provides a possibility to detect copies in a continuous query video stream. However, its computational complexity...     

污水处理厂污泥厌氧消化优化设计与运行探讨

对国内典型的污水处理厂污泥厌氧消化系统进行调研发现,厌氧消化系统的运行管理尚存在诸多问题。在总结白龙港污水处理厂污泥厌氧消化调试与运行管理经验的基础上,针对厌氧消化工艺存在的普遍性问题,提出了厌氧消化系统设计和运行管理方面的建议,以促进工艺设计优化,提高运行管理水平。