Matrix DecompositionDiagonal Dominant MatricesIn this paper we consider mixed (fast stochastic approximation and deterministic refinement) algorithms for Matrix Inversion (MI) and Solving Systems of Linear Equations (SLAE). Monte Carlo methods are used for the stochastic approximation, since it is known ...
Recently, processor efficiency of the previous parallel algorithms for numerical matrix inversion has been substantially improved in (Pan and Reif, 1987), reaching optimum estimates up to within a logarithmic factor; that work, however, applies neither to the evaluation of the determinant and the ...
A parallel algorithm for Householder Transformation is given in this paper. Based on the parallelizing of Householder Transformation, we propose the algorithms for solving ill-conditioned matrix inversion, ill-conditioned linear system and ill-conditioned least squares. The proposed methods have acceptable...
The divide and conquer approach is applied in the following algorithms − Binary search Quick sort Merge sort Integer multiplication Matrix inversion Matrix multiplication Greedy Method In greedy algorithm of optimizing solution, the best solution is chosen at any moment. A greedy algorithm is very ...
The number of parallel steps can be reduced to O(log( m n+1)) over an arbitrary field using polynomial circuits for the operations in the extended field of constants. These methods can be extended to the design of parallel algorithms of the same asymptotic complexity for the inversion of ...
Csanky, L.: Fast Parallel Matrix Inversion Algorithms, SIAM J. Comput. 5 (1976), 618 (/39/). Article MATH MathSciNet Google Scholar Chen, S.C., Kuck, D.J., and Sameh, A.H.: Practical Parallel Triangular System Solvers, ACM TOMS 4 (1978), 270 (/132/). Article MATH MathSci...
Besides the progress in algorithms and processor speed, one possible approach to solve these problem is to add sensors (i.e. to have more than n sensors for a n-DOF manipulator) to obtain information, allowing a faster calculation of the current pose of the platform, at the cost of more...
Deep learning-based medical image segmentation has made great progress over the past decades. Scholars have proposed many novel transformer-based segmentation networks to solve the problems of building long-range dependencies and global context connectio
On the scientific application side, most algorithms require only a few operations or flops compared to the amount of numbers or bytes accessed from main memory, and thus are significantly memory bound. The Flop/s metric is no longer the most adequate for reporting the application performance of ...
In this section, we compare the computational cost of our proposed PN-SVM with other related algorithms. During the training phase, similar to conventional SVM, the proposed model needs to find coefficients of n constraints for solving the dual QPP. Hence the computation of the proposed model is...