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SIAMJO
2000

Superlinear Convergence and Implicit Filtering

13 years 11 months ago
Superlinear Convergence and Implicit Filtering
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. Key words. noisy optimization, implicit filtering, BFGS algorithm, superlinear convergence AMS subject classifications. 65K05, 65K10, 90C30
T. D. Choi, C. T. Kelley
Added 19 Dec 2010
Updated 19 Dec 2010
Type Journal
Year 2000
Where SIAMJO
Authors T. D. Choi, C. T. Kelley
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