Abstract. The QR decomposition with column pivoting (QRP) of a matrix is widely used for numerical rank revealing in applications. The performance of LAPACK implementation (DGEQP3) of the Householder QRP algorithm is limited by Level 2 BLAS operations required for updating the column norms

The QR factorization with column pivoting (QRP), originally suggested by Golub and Businger in 1965, is a popular approach to computing rank-revealing factorizations. Using BLAS Level 1, it was implemented in LINPACK, and, using BLAS Level 2, in LAPACK. While the BLAS Level2version delivers

A Quick Response Program (QRP) was developed and implemented at St George Hospital during 1995 and 1996. The program sought to improve the service provided to elderly people presenting to the emergency department by offering a new rapid response service pathway to community-based care. Emergency

this paper, we focus on the selforganization of a community structure based on user preferences for P2P systems. We propose these methods to improve P2P search performance: 1) Extended Pong,2)Pong Proxy, 3) QRP with Firework 4) Backward Learning, and 5) Community Self-Organization Algorithm. We

. In this paper, as a case study, we present an HPMO-avoiding algorithm for the Green’s function calculation in quantum Monte Carlo simulation. The original algorithm utilizes the QRdecomposition with column pivoting (QRP) as computational kernel. QRP is an HPMO. The redesigned algorithm maintains the same

Algebraic algorithms deal with numbers, vectors, matrices, polynomials, for-mal power series, exponential and differential polynomials, rational functions, algebraic sets, curves and surfaces. In this vast area, manipulation with matri-ces and polynomials is fundamental for modern computations in Sciences and

This is a preliminary version of a Chapter on Algebraic Algorithms in the up-