An algorithm was proposed, using sparse matrix as the storage structure of the data. To prevent the critical path from being lost, the queue method was adopted for the operation. Compared with the classical algorithm, this algorithm is simple, with close asymptotic time complexity (O (n e~2...
GP-GPUs have been used as the platform for many applications due to their powerful computation ability and massively parallel features. In this paper, we first investigate the CSR sparse matrix format, the performance of existing optimized SpMV (Sparse matrix-vector multiplication) algorithms, and an...
We describe an efficient implementation of an algorithm for computing selected elements of a general sparse symmetric matrix A that can be decomposed as A = LDLT where L is lower triangular and D is diagonal. Our implementation, which is called SelInv, is built on top of an efficient supermo...
Element positions are adjusted for iterations, to select the optimal array configuration. Following sparse layout optimization, the simulated 64-element planar radio antenna array shows that the maximum sidelobe level decreases by 1.79 dB, and the beamwidth narrows by 3°. Within the scan range of ...
While computational approaches exist to infer cell phase directly from single-cell RNA-sequencing data, existing methods yield poor circadian phase estimates, and do not quantify estimation uncertainty, which is essential for interpretation of results from very sparse single-cell RNA-sequencing data. To...
(2006) proposed a satellite selection algorithm based on SVD matrix decomposition. Methods of this type have low computational complexity, run quickly, and are convenient for selecting satellites through mathematical analysis, equivalent substitution, and weighting methods based on matrix theory. However,...
Filter gain for Kalman filtering algorithm based on Wiener filter: (6)Bt=R^t/t−1MtTMtR^t/t−1MtT+Qt−1 Filter gain for new type filtering algorithm based on projection filter: (7)Bt=MtTQt−1Mt−1MtTQt−1 Covariant matrix equation of estimation error for Kalman filtering algori...
and perform a few elementary matrix additions. Two important remarks are in order: 1. Sparsity. For each ≥ 0, vanishes outside of Ω and is, therefore, sparse, a fact which can be used to evaluate the shrink function rapidly. 2. Low-rank property. The matrices turn out to have lo...
However, since sample 2 does not demonstrate sparse blinking, it is interesting to consider if MUSICAL can be applied to such sample using only a few frames. Owing to the sliding window of size N pixels, the rank of the matrix on which eigenimages are computed is less than or equal to ...
reformat using prettier Jun 13, 2017 thumbnail.png LAP-JV Jan 4, 2017 README LAP-JV Linear Assignment Problem — algorithm by R. Jonker and A. Volgenant “A shortest augmenting path algorithm for dense and sparse linear assignment problems,” by R. Jonker and A. Volgenant,Computing(1987) ...