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Title: Photoinjector Optimization Using A Derivative-Free Model-Based, Trust-Region Algorithm For The Argonne Wakefield Accelerator

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Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
DOE Contract Number:
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Resource Relation:
Conference: 8th International Particle Accelerator Conference (IPAC 2017), 05/14/17 - 05/19/17, Copenhagen, DK
Country of Publication:
United States

Citation Formats

Neveu, N., Larson, J., Power, J. G., and Spentzouris, L.. Photoinjector Optimization Using A Derivative-Free Model-Based, Trust-Region Algorithm For The Argonne Wakefield Accelerator. United States: N. p., 2017. Web. doi:10.1088/1742-6596/874/1/012062.
Neveu, N., Larson, J., Power, J. G., & Spentzouris, L.. Photoinjector Optimization Using A Derivative-Free Model-Based, Trust-Region Algorithm For The Argonne Wakefield Accelerator. United States. doi:10.1088/1742-6596/874/1/012062.
Neveu, N., Larson, J., Power, J. G., and Spentzouris, L.. Sat . "Photoinjector Optimization Using A Derivative-Free Model-Based, Trust-Region Algorithm For The Argonne Wakefield Accelerator". United States. doi:10.1088/1742-6596/874/1/012062.
title = {Photoinjector Optimization Using A Derivative-Free Model-Based, Trust-Region Algorithm For The Argonne Wakefield Accelerator},
author = {Neveu, N. and Larson, J. and Power, J. G. and Spentzouris, L.},
abstractNote = {},
doi = {10.1088/1742-6596/874/1/012062},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2017},
month = {Sat Jul 01 00:00:00 EDT 2017}

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  • This study presents a trust region algorithm to minimize a function f when one has access only to noise-corrupted function values f¯. The model-based algorithm dynamically adjusts its step length, taking larger steps when the model and function agree and smaller steps when the model is less accurate. The method does not require the user to specify a fixed pattern of points used to build local models and does not repeatedly sample points. If f is sufficiently smooth and the noise is independent and identically distributed with mean zero and finite variance, we prove that our algorithm produces iterates suchmore » that the corresponding function gradients converge in probability to zero. As a result, we present a prototype of our algorithm that, while simplistic in its management of previously evaluated points, solves benchmark problems in fewer function evaluations than do existing stochastic approximation methods.« less
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