, Volume 87, Issue 2, pp 215-249

A primal-dual trust-region algorithm for non-convex nonlinear programming

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A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.

Received: July 19, 1999 / Accepted: February 1, 2000¶Published online March 15, 2000