The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results ...The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.展开更多
A connected and undirected graph model of active distribution networks with considering the function of interconnecting switches is constructed in this paper.Based on this model,the island partition problem of active ...A connected and undirected graph model of active distribution networks with considering the function of interconnecting switches is constructed in this paper.Based on this model,the island partition problem of active distribution networks can be described as a 1-neighbour knapsack problem.An effective heuristic algorithm named prospective greedy algorithm is then proposed to solve this problem.Case studies on PG&E 69-bus network show the validity of the proposed model and algorithm.展开更多
从一维细胞自动机模型入手,设计了一种求解二元离散优化问题的二元粒子群算法细胞自动机模型(BPSO-CA)。粒子从起始细胞出发,根据本身携带的信息并感知存储在细胞中的全局最优粒子位置的信息随机选择状态(0或1),从而实现复杂智能的"...从一维细胞自动机模型入手,设计了一种求解二元离散优化问题的二元粒子群算法细胞自动机模型(BPSO-CA)。粒子从起始细胞出发,根据本身携带的信息并感知存储在细胞中的全局最优粒子位置的信息随机选择状态(0或1),从而实现复杂智能的"涌现"。然后将其用来求解多维0/1背包问题,同时引入贪心算法对不符合约束条件的非法个体进行修正。通过对Zuse Institute Berlin公布的测试集进行实验,表明该模型能在多项式时间内完成求解过程,且实验结果优于测试集记录的结果。展开更多
文摘The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.
文摘A connected and undirected graph model of active distribution networks with considering the function of interconnecting switches is constructed in this paper.Based on this model,the island partition problem of active distribution networks can be described as a 1-neighbour knapsack problem.An effective heuristic algorithm named prospective greedy algorithm is then proposed to solve this problem.Case studies on PG&E 69-bus network show the validity of the proposed model and algorithm.
文摘从一维细胞自动机模型入手,设计了一种求解二元离散优化问题的二元粒子群算法细胞自动机模型(BPSO-CA)。粒子从起始细胞出发,根据本身携带的信息并感知存储在细胞中的全局最优粒子位置的信息随机选择状态(0或1),从而实现复杂智能的"涌现"。然后将其用来求解多维0/1背包问题,同时引入贪心算法对不符合约束条件的非法个体进行修正。通过对Zuse Institute Berlin公布的测试集进行实验,表明该模型能在多项式时间内完成求解过程,且实验结果优于测试集记录的结果。
