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...
Source code of the IPDPS '21 paper: "TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on GPUs" by Yuyao Niu, Zhengyang Lu, Meichen Dong, Zhou Jin, Weifeng Liu, and Guangming Tan. - SuperScientificSoftwareLaboratory/TileSpMV
When the matrix is sparse this method works fine because sparse matrices take less time to compute. It is not practically possible as it is computation and theoretical approach only. It takes more space for storing sub matrices. There is less chance of accuracy. Chat on Discord ...
turning portions of the sparse matrix into dense blocks and invoking high-performance BLAS/lapack libraries. It is designed with optimization libraries for Levenberg-Marquardt in mind, and aims at reducing part of the complexity offering the best tool for the job. Compared to the library currently...
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...
To solve the problem, this paper presents a spatial distance-based spatial clustering algorithm for sparse image data (SDBSCA-SID). Firstly, the imaging range of the image sensor constitutes a two-dimensional (2D) constraint space. Under the constraint, spatial clustering was carried out based ...
This paper proposes a primal decomposition algorithm for efficient computation of multistage scenario model predictive control, where the future evolution of uncertainty is represented by a scenario tree. This often results in large-scale optimization problems. Since the different scenarios are only coupled...
The coupling coefficient matrix of the Ising model is mapped to the point convolution kernel. Divide the J into n 1 × 1 convolution ker- nels with n channels by row. Through the residual structure, the addition operation required in the algorithm is completed. By continuously calling ...
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...
The matrix completion problem is to recover a low-rank matrix from a subset of its entries. The main solution strategy for this problem has been based on n