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

, in general, superior performance, it may result in worse performance for large matrix sizes due to cache e ects. We introduce a modi cation of the QRP algorithm which allows the use of BLAS Level 3 kernels while maintaining the numerical behavior of the LINPACK and LAPACK implementations. Experimental

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

. 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

Abstract. Given a complex matrix H, we consider the decomposition H = QRP ∗ , where R is upper triangular and Q and P have orthonormal columns. Special instances of this decomposition include (a) the singular value decomposition (SVD) where R is a diagonal matrix containing the singular values

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

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