We propose a technique of multisensor signal reconstruction based on the assumption, that source signals are spatially sparse, as well as have sparse [wavelet-type] representation in time domain. This leads to a large scale convex optimization problem, which involves l1 norm minimization. The optimization is carried by the Truncated Newton method, using preconditioned Conjugate Gradients in inn...

In a recent article, the first and second kinds of multivariate Chebyshev polynomials fractional degree, relevant integral repesentations, have been studied. this we introduce pseudo-Lucas functions show possible applications these new functions. For kind, compute Newton sum rules any orthogonal polynomial set starting from entries Jacobi matrix. representation formulas for powers r×r matrix, a...

Newton’s arguments for the immobility of the parts of absolute space have been used to licence several proposals concerning his metaphysics. This paper clarifies Newton. Notably, it demonstrates, contrary to Nerlich (2005), that Newton does not appeal to the identity of indiscernibles, but rather to a view about de re representation. Additionally, I show, contrary to DiSalle (1994), that the ar...

a new approach utilizing newton method and homotopy analysis method (ham) is proposed for solving nonlinear system of equations. accelerating the rate of convergence of ham, and obtaining a global quadratic rate of convergence are the main purposes of this approach. the numerical results demonstrate the efficiency and the performance of proposed approach. the comparison with conventional homoto...

In this paper, we present a full Newton step feasible interior-pointmethod for circular cone optimization by using Euclidean Jordanalgebra. The search direction is based on the Nesterov-Todd scalingscheme, and only full-Newton step is used at each iteration.Furthermore, we derive the iteration bound that coincides with thecurrently best known iteration bound for small-update methods.

Hessian information speeds convergence substantially in motion optimization. The better the Hessian approximation the better the convergence. But how good is a given approximation theoretically? How much are we losing? This paper addresses that question and proves that for a particularly popular and empirically strong approximation known as the Gauss-Newton approximation, we actually lose very ...

Abstract—The solution of non linear problems are based on very well grounded mathematical theories. This document proposes a Newton step graph-based model for convex non linear separable problems (NLP) with linear constraints. The Newton step is well suited for this kind of problems, but when the problem size grows the NLSP model will grow in a non linear manner. Furthermore, the constraints ha...

The use of Direct Current (DC) transmission links in power systems is increasing continuously. Thus, it important to develop new techniques model the inclusion these devices network analysis, order allow studies operation and expansion planning large-scale electric systems. In this context, main objective paper present a methodology for simultaneous AC-DC flow multi-terminal High Voltage (HVDC)...