Superlinear Convergence and Implicit Filtering
نویسندگان
چکیده
In this note we show how the implicit filtering algorithm can be coupled with the BFGS quasi-Newton update to obtain a superlinearly convergent iteration if the noise in the objective function decays sufficiently rapidly as the optimal point is approached. We show how known theory for the noise-free case can be extended and thereby provide a partial explanation for the good performance of quasi-Newton methods when coupled with implicit filtering.
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ورودعنوان ژورنال:
- SIAM Journal on Optimization
دوره 10 شماره
صفحات -
تاریخ انتشار 2000