New parallel algorithms for finding determinants of N&amp;#x00D7;N matrices

Almalki, Sami G.; Alzahrani, Saleh; Alabdullatif, Abdullatif

doi:10.1109/wccit.2013.6618713

Cited by 6 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In 2014, Almalki and his workmates designed parallel algorithms for Laplace expansion and LU decomposition. The algorithms are 129% and 44% respectively faster than the sequential algorithms [34].…”

Section: Computation By Special Meansmentioning

confidence: 94%

How Difficult To Compute Coefficients of Characteristic Polynomial?

2016

International Journal of Research Studies in Computer Science a

View full text Add to dashboard Cite

show abstract

Section: Computation By Special Meansmentioning

confidence: 94%

How Difficult To Compute Coefficients of Characteristic Polynomial?

2016

International Journal of Research Studies in Computer Science a

View full text Add to dashboard Cite

show abstract

“…Ese mismo año Yi-Gang Tai publicó sobre la aceleración de las operaciones matriciales con algoritmos pipeline y la reducción de vectores (Tai, 2012). En 2013, Sami Almaki presentó un nuevo algoritmo paralelo para la resolución de determinantes de orden nxn (Almalki, 2013). Por último, el año pasado fue publicado por Xinyu Lei, el artículo sobre el paradigma del cómputo en el servicio de la nube, enfocándose en el caso de calcular determinantes de grandes matrices (Lei, 2014).…”

Section: Trabajos Previosunclassified

Implementación de un circuito custom DSP en FPGAs para cálculo del determinante 3x3, y matriz inversa de matrices ortogonales 3x3

Jáuregui

Panduro

Ortega

et al. 2015

recibe

View full text Add to dashboard Cite

En este artículo se presenta el diseño e implementación de un circuito digital a medida para el cálculo de determinantes de orden 3x3 y matriz inversa de matrices ortogonales 3x3. Se analizan los resultados de la implementación de los circuitos en dos plataformas de familias de dispositivos reconfigurables, estas son Artix 7 y Spartan 6 Low-Power, en los que se comparan la ocupación y los tiempos de respuesta. La descripción del circuito se realizó en Lenguaje de Descripción de Hardware (HDL).

show abstract

“…Finding the inverse of a square matrix can be done using a variety of methods, including well-known methods such as Gauss Elimination, Gauss-Jordan, LU Decomposition, QR Decomposition and Cholesky Decomposition [10].…”

Section: Introductionmentioning

confidence: 99%

Parallel Algorithm for Finding Inverse of a Matrix and its Application in Message Sharing (Coding Theory)

Saraf¹,

Dhingra²,

Pinheiro³

2016

IJCA

View full text Add to dashboard Cite

A parallel algorithm for finding the inverse of the matrix using Gauss Jordan method in OpenMP. The Gauss Jordan method has been chosen for this project because it provides a direct method for obtaining inverse matrix and requires approx. 50% fewer operations unlike other methods. Hence forth it is suitable for massive parallelization. Then, authors have analyzed the parallel algorithm for computing the inverse of the matrix and compared it with its perspective sequential algorithm in terms of run time, speed-up and efficiency. Further, the obtained result is used to propose a new method of Message Sharing (called Coding Theory). The proposed method is simple and has a great potential to be applied to other situation where the exchange of messages is done confidentially such as in military operation, banking transactions etc.

show abstract

New parallel algorithms for finding determinants of N×N matrices

Cited by 6 publications

References 5 publications

How Difficult To Compute Coefficients of Characteristic Polynomial?

How Difficult To Compute Coefficients of Characteristic Polynomial?

Implementación de un circuito custom DSP en FPGAs para cálculo del determinante 3x3, y matriz inversa de matrices ortogonales 3x3

Parallel Algorithm for Finding Inverse of a Matrix and its Application in Message Sharing (Coding Theory)

Contact Info

Product

Resources

About