A primal-dual trust-region algorithm for non-convex nonlinear programming
- Cite this article as:
- Conn, A., Gould, N., Orban, D. et al. Math. Program. (2000) 87: 215. doi:10.1007/s101070050112
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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.
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