We present a new supervised classification algorithm using boosting with support vector machines (SVM) and able to deal with very large data sets. Training a SVM usually needs a quadratic programming, so that the learning task for large data sets requires large memory capacity and a long time. Proximal SVM proposed by Fung and Mangasarian is another SVM formulation very fast to train because it...

This talk presents numerical methods recently developed for complex creeping flows. A Stokes fluid is considered in interaction with moving obstacles. This fluid is non-Newtonian and its rheology depends on the fluid’s composition, this heterogeneity is transported by the flow. To solve the resulting 3D set of non-linear PDEs [6] dedicated numerical algorithms have been developed they are based...

The capacitance matrix method has been widely used as an efficient numerical tool for solving the boundary value problems on irregular regions. Initially, this method was based on the Sherman–Morrison–Woodbury formula, an expression for the inverse of the matrix (A + UV ) with A ∈ <n×n and U,V ∈ <n×p. Extensions of this method reported in literature have made restrictive assumptions on the matr...

In studying the vibration of fast trains, we encounter a palindromic quadratic eigenvalue problem (QEP) (λ2AT +λQ+A)z = 0, where A,Q ∈ Cn×n and QT = Q. Moreover, the matrix Q is block tridiagonal and block Toeplitz, and the matrix A has only one nonzero block in the upperright corner. So most of the eigenvalues of the QEP are zero or infinity. In a linearization approach, one typically starts w...

Our randomized preprocessing enables pivoting-free and orthogonalization-free solution of homogeneous linear systems of equations, which leads to significant acceleration of the known algorithms in the cases of both general and structured input matrices. E.g., in the case of Toeplitz inputs, we decrease the estimated solution time from quadratic to nearly linear, and our tests show dramatic dec...

Abstract Efficient use of global navigation satellite system (GNSS) observations improves when applying rational selection algorithms. By combining the Sherman–Morrison formula and singular value decomposition, a smaller-GDOP (geometric dilution precision)-value method is proven for an increasing number visible satellites. this smaller-GDOP-value with maximum-volume-tetrahedron method, new rapi...

In this paper, updating algorithms for solving linear systems of equations are presented using a systolic array model. First, a parallel algorithm for computing the inverse of rank-one modiied matrix using the Sherman-Morrison formula is proposed. This algorithm is then extended to solving the updated systems of linear equations on a linear systolic array. Finally, the generalisation to the upd...

In this work we consider the approximate inverse preconditioners AISM based on the Sherman-Morrison formula [1]. We will show some of theoretical properties of the factors of AISM in function of the value of parameter s considered in the decomposition. In particular, we will show that one of the factors hides a scaled factor of the standard incomplete LDU decomposition of the system matrix A. T...

This paper deals with the control of vibrations an automatic cutting machine a cartesian kinematic chain. A particular application method structural modifications is considered, since small number frequency response function available. The Sherman-Morrison formula used to predict effect tuned vibration absorber (TVA) on generated by tool; advantages and limits this are discussed. In order impro...