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Many real applications can be formulated as nonlinear minimization problems with a single linear equality constraint and box constraints. We are interested in solving problems where the number of variables is so huge that basic operations, such as the evaluation of the objective function or the updating of its gradient, are very time consuming. Thus, for the considered class of problems (including dense quadratic programs), traditional optimization methods cannot be applied directly. In this paper, we define a decomposition algorithm model which employs, at each iteration, a descent search direction selected among a suitable set of sparse feasible directions. The algorithm is characterized by an acceptance rule of the updated point which on the one hand permits to choose the variables to be modified with a certain degree of freedom and on the other hand does not require the exact solution of any subproblem. The global convergence of the algorithm model is proved by assuming that the objective function is continuously differentiable and that the points of the level set have at least one component strictly between the lower and upper bounds. Numerical results on large-scale quadratic problems arising in the training of support vector machines show the effectiveness of an implemented decomposition scheme derived from the general algorithm model. [ABSTRACT FROM AUTHOR]

In engineering applications, the singularity, and the forward and inverse displacement of spatial parallel manipulators have received a great deal of attention. This paper presents a new simple but effective methodology to investigate these problems based on the analysis of the degree of freedom (DoF) of the spatial parallel manipulator. Through numerical method, the singularity and the forward displacement problems can be cracked simultaneously. With a numerical example, we point out that the Newton-GMRES algorithm is quite a good redeeming method to solve the forward displacement of spatial parallel manipulators, in which cases the differential coefficient matrix of the Newton−Raphson method is nearly singular. [ABSTRACT FROM AUTHOR]