An Interior Point Algorithm for Large-Scale Nonlinear Programming

The design and implementation of a new algorithm for solving large nonlinear programming problems is described. It follows a barrier approach that employs sequential quadratic programming and trust regions to solve the subproblems occurring in the iteration. Both primal and primal-dual versions of the algorithm are developed, and their performance is illustrated in a set of numerical tests. Key words: constrained optimization, interior point method, large-scale optimization, nonlinear programming, primal method, primal-dual method, sequential quadratic programming, barrier method, trust region method. # Original manuscript: July 27, 1997. + Computer Science Department, University of Colorado, Boulder CO 80309. This author was supported by ARO grant DAAH04-94-0228, and AFOSR grant F49620-94-1-0101. # CAAM Department, Rice University, Houston TX 77005. This author was supported by Department of Energy grant DE-FG02-87ER25047-A004. ECE Department, Northwestern University, Evanston Il ...