Abstract
A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to simple bounds and linear equality constraints. The method alternates between a classical primal-dual step and a Newton-like modified barrier step in order to ensure descent on a suitable merit function. Convergence of a well-defined subsequence of iterates is proved from arbitrary starting points. Preliminary numerical results are presented.
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Conn, A.R., Gould, N.I.M., Toint, P.L. (2000). A primal-dual algorithm for minimizing a non-convex function subject to bound and linear equality constraints. In: Pillo, G.D., Giannessi, F. (eds) Nonlinear Optimization and Related Topics. Applied Optimization, vol 36. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3226-9_2
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DOI: https://doi.org/10.1007/978-1-4757-3226-9_2
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