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Interior-point methods for nonconvex nonlinear programming: Regularization and warmstarts. (English) Zbl 1181.90243
Summary: In this paper, we investigate the use of an exact primal-dual penalty approach within the framework of an interior-point method for nonconvex nonlinear programming. This approach provides regularization and relaxation, which can aid in solving ill-behaved problems and in warmstarting the algorithm. We present details of our implementation within the loqo algorithm and provide extensive numerical results on the CUTEr test set and on warmstarting in the context of quadratic, nonlinear, mixed integer nonlinear, and goal programming.
MSC:
90C30Nonlinear programming
90C51Interior-point methods
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