Iterative algorithmA sparse matrix technology is used in computing the solution of state space equation with a sparse matrix of coefficients in Max_Algebra. And a special iterative algorithm is developed for the convenience of application of sparse matrix technology.doi:10.1016/S1474-6670(17)69356-XYu XiaofengXu XinheIFAC Pro...
Briefly, given am×n matrixA for which a low-rank approximation is required, the basic idea of probabilistic low-rank approximation algorithms is to perform the following two steps [7]: In detail, the first step is performed by factorizing matrixAΩ using QR decomposition [6], whereΩ is ...
Matrix optimization has various applications in finance, statistics, and engineering, etc. In this paper, we derive the Lagrangian dual of the matrix optimization problem with sparse group lasso regularization, and develop an adaptive gradient/semismooth Newton algorithm for this dual. The algorithm ada...
and algorithm for each sparse tile to improve performance from the perspective of the local sparse structure of the matrix. In addition, nonzeros in very sparse tiles are extracted into a separate matrix for better performance. TileSpMV provides a version of CUDA on a high parallelism currently...
About determinism: assuming BLAS is deterministic, BaSpaCho will be 100% deterministic on the CPU, but not on CUDA if there is any "sparse elimination" set of parameters, because both factor and solve operations use atomic addition for parallelism on the GPU. Also, a Cuda architecture >=6 ...
Second, existing approaches do not quantify the uncertainty of phase estimates. This becomes crucial for interpretation of results from very sparse droplet-based scRNA-seq data. Third, run times of existing approaches scale poorly with the number of cells, making analysis of droplet-based scRNA-seq...
Subspace outlier detection has emerged as a practical approach for outlier detection. Classical full space outlier detection methods become ineffective in high dimensional data due to the “curse of dimensionality”. Subspace outlier detection methods ha
Furthermore, in addition to returning the final solution, the algorithm also generates individuals with the highest accuracy for each analyzed number of features. Due to this feature, the algorithm’s user can decide by himself which feature subset is preferable, larger but more precise or less ...
where ⊕ is component-wise addition modulo 2. • The “coin tossing” operator C is chosen to be the Grover rotation without the oracle (see Lemma 2.3.1): C=2|ψd〉〈ψd|−Iwhere I is the identity operator in Hd and |ψd〉 is the equal superposition over all n directions: ...
In addition to increasing computational resources with high- capacity such as GPU to tackle computational complexity issues, further refinement and enhancement to the clustering algorithm might reduce complexity. This became necessary considering each clustering algorithm’s different computational complexity, ...