In this report we propose a new recursive matrix formulation of limited memory variable metric methods. This approach enables to approximate of both the Hessian matrix and its inverse and can be used for an arbitrary update from the Broyden class (and some other updates). The new recursive formulation requires approximately 4mn multiplications and additions for the direction determination, so i...

In the framework of elastic shape analysis, a shape is invariant to scaling, translation, rotation and reparameterization. Since this framework does not yield a closed form of geodesic between two shapes, iterative methods have been proposed. In particular, path straightening methods have been proposed and used for computing a geodesic that is invariant to curve scaling and translation. Path st...

Joint decisions in production allocation and ordering policies for single and multiple products in a production-distribution network system consisting of multiple plants are discussed, production capacity constraints of multi-plants and unit production capacity for producing a product are considered. Based on the average total cost in unit time, the decisive model is established. It tries to de...

This paper considers a distributed network of through-the-wall imaging radars and provides a solution for accurate indoor scene reconstruction in the presence of multipath propagation. A sparsity-based method is proposed for eliminating ghost targets under imperfect knowledge of interior wall locations. Instead of aggregating and processing the observations at a central fusion station, joint sc...

We propose a quasi-Newton algorithm for solving large optimization problems with nonlinear equality constraints. It is designed for problems with few degrees of freedom, and is motivated by the need to use sparse matrix factorizations. The algorithm incorporates a correction vector that approximates the cross term Z^WYpy in order to estimate the curvature in both the range and null spaces of th...

We propose a quasi Newton algorithm for solving large optimization problems with nonlinear equality constraints It is designed for problems with few degrees of freedom and is motivated by the need to use sparse matrix factorizations The algorithm incorporates a correction vector that approximates the cross term ZWY pY in order to estimate the curvature in both the range and null spaces of the c...

Quasi-Newton algorithms for unconstrained nonlinear minimization generate a sequence of matrices that can be considered as approximations of the objective function second derivatives. This paper gives conditions under which these approximations can be proved to converge globally to the true Hessian matrix, in the case where the Symmetric Rank One update formula is used. The rate of convergence ...

in this research optimal reconfiguration strategy of the improved srr reconfigurable mobile robot based on force-angle stability measure has been designed using genetic algorithm. path tracking nonlinear controller which keeps robot’s maximum stability has been designed and simulated in matlab. motion equations of the robot have been derived in parametric form by means of newton- euler, lagrang...

We propose a quasi-Newton algorithm for solving large optimization problems with nonlinear equality constraints. It is designed for problems with few degrees of freedom and is motivated by the need to use sparse matrix factorizations. The algorithm incorporates a correction vector that approximates the cross term Z T WY p Y in order to estimate the curvature in both the range and null spaces of...

A new algorithm for signal reconstruction in a compressive sensing framework is presented. The algorithm is based on minimizing an unconstrained regularized p norm with p < 1 in the null space of the measurement matrix. The unconstrained optimization involved is performed by using a quasi-Newton algorithm in which a new line search based on Banach’s fixed-point theorem is used. Simulation resul...