Andrew R. Conn, Katya Scheinberg, et al.
IMA Journal of Numerical Analysis
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.
Andrew R. Conn, Katya Scheinberg, et al.
IMA Journal of Numerical Analysis
Andrew R. Conn, Katya Scheinberg, et al.
SIAM Journal on Optimization
Andreas Wächter, Chandu Visweswariah, et al.
Future Generation Computer Systems
Andrew R. Conn, Luís N. Vicente, et al.
SIAM Journal on Optimization