A superlinearly convergent method for minimization problems with linear inequality constraints

A method is described for minimizing a continuously differentiable functionF(x) ofn variables subject to linear inequality constraints. It can be applied under the same general assumptions as any method of feasible directions. IfF(x) is twice continuously differentiable and the Hessian matrix ofF(x) has certain properties, then the algorithm generates a sequence of points which converges superlinearly to the unique minimizer ofF(x). No computation of secondorder derivatives is required.Sponsored by the United States Army under Contract No. DA-31-124-ARO-D-462 and by the National Science Foundation under Research Grant GP.33033.