Journal of Applied Mathematics (Jan 2013)
A Generalized Gradient Projection Filter Algorithm for Inequality Constrained Optimization
Abstract
A generalized gradient projection filter algorithm for inequality constrained optimization is presented. It has three merits. The first is that the amount of computation is lower, since the gradient matrix only needs to be computed one time at each iterate. The second is that the paper uses the filter technique instead of any penalty function for constrained programming. The third is that the algorithm is of global convergence and locally superlinear convergence under some mild conditions.