Error Bounds and Superlinear Convergence Analysis of Some Newton-Type Methods in Optimization

We show that, for some Newton-type methods such as primal-dual interior-point path following methods and Chen-Mangasarian smoothing methods, local superlinear convergence can be shown without assuming the solutions are isolated. The analysis is based on local error bounds on the distance from the iterates to the solution set.

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