A Modified SQP Parallel Variable Distribution Algorithm
修正的SQP型并行变量分配算法

Ferris and Mangasarian proposed a PVD(parallel variable distribution) algorithm for solving optimization problems, which divides variables into primary and secondary variables groups. According to the algorithm, the variables are distributed among p parallel processors with each processor having the responsibility for updating its primary variables while allowing the remaining "secondary" variables to change in a restricted fashion along some easily computable directions, which enhances robustness and flexibility of the algorithm. In this paper, we present a modified SQP type PVD algorithm based on 6], whose search direction is a suitable combination of a descent direction and a feasible direction, and give a second-order revised for such a direction. This new algorithm is very effective in preventing Maratos effect from happening, and avoid constraints in subproblem are inconsistent. We show the global convergence under some suitable conditions.