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首页> 外文期刊>Mathematical methods of operations research >An inexact proximal regularization method for unconstrained optimization
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An inexact proximal regularization method for unconstrained optimization

机译:An inexact proximal regularization method for unconstrained optimization

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摘要

We present a regularization algorithm to solve a smooth unconstrained minimization problem. This algorithm is suitable to solve a degenerate problem, when the Hessian is singular at a local optimal solution. The main feature of our algorithm is that it uses an outer/inner iteration scheme. We show that the algorithm has a strong global convergence property under mild assumptions. A local convergence analysis shows that the algorithm is superlinearly convergent under a local error bound condition. Some numerical experiments are reported.

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