• σi(A) denotes the ith largest singular value of A, σmin(A) denotes the minimal nonzero singular value of A and σmax(A) denotes the maximal singular value of A; • If U has orthogonal columns, that is, UTU = I, then ‖Ux‖ = ‖x‖ and ‖U>y‖ ≤ ‖y‖; • ‖Ax‖ ≤ σmax(A)‖x‖, where σmax(A) denotes the largest singular value of A; • Note that we do NOT have in general ‖Ax‖ ≥ σmin(A)‖x‖, where σmin(A)(> 0) d...

in this paper, modi cation of the optimal homotopy asymptotic method (moham) is appliedupon singular initial value lane-emden type equations and results are compared with the available exactsolutions. the modi ed algorithm give the exact solution for dierential equations by using one iterationonly.

with rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. because o...

in this paper a mathematical simulation of a simplified internal combustion engine is presented. to contribute engine kinematics and its geometry, simple relations are derived for constrained motions. the equation of motion for the piston forms a singular boundary value problem. the uniqueness of the solution was studied in the banach space. for solving governing equations an iterative numerica...

disguised face recognition is a major challenge in the field of face recognition which has been taken less attention. therefore, in this paper a disguised face recognition algorithm based on local phase quantization (lpq) method and singular value decomposition (svd) is presented which deals with two main challenges. the first challenge is when an individual intentionally alters the appearance ...

This article presents a new subspace-based technique for reducing the noise of signals in time-series. In the proposed approach, the signal is initially represented as a data matrix. Then using Singular Value Decomposition (SVD), noisy data matrix is divided into signal subspace and noise subspace. In this subspace division, each derivative of the singular values with respect to rank order is u...

Abstract : A new algorithm for noise filtering, based on non-linear processing of image in blocks, using singular value decomposition (SVD) is presented in this paper. Noise filtering is performed in the domain of singular values and singular vectors. A priori noise variance knowledge is not required, because a singular value-based noise variance estimation is performed in the first phase of th...

The computation (or approximation) of some of the smallest or largest singular values of a large sparse matrix is a big challenge, having many important applications. In this talk we discuss approximations to singular triples that can be obtained from subspaces. One of the topics is the concept of harmonic singular triples, introduced in [1]. The word “harmonic” expresses the fact that we try t...

the grace mission has provided scientific community time-variable gravity field solutions with high precision and on a global scale. the grace mission was launched on march 2003. this mission consists of two satellites that pursue each other in their orbit. distance between two satellites in orbit is measured continuously to an accuracy of better than 1 micron using kbr system placed in satelli...