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linearized Bayesian inference. ACM Transactions on Mathematical
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https://doi.org/10.21105/joss.00940</unstructured_citation>
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superlinear convergence for truncated iterated projections methods.
Mathematical Programming, 27, 176–190.
https://doi.org/10.1007/BF02591944</unstructured_citation>
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(1996). Choosing the forcing terms in an inexact Newton method. SIAM
Journal on Scientific Computing, 17(1), 16–32.
https://doi.org/10.1137/0917003</unstructured_citation>
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<cYear>2021</cYear>
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Taylor approximation for chance constrained optimization problems
governed by partial differential equations with high-dimensional random
parameters. SIAM/ASA Journal on Uncertainty Quantification, 9(4),
1381–1410. https://doi.org/10.1137/20M1381381</unstructured_citation>
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<journal_title>Journal of Open Source
Software</journal_title>
<issue>38</issue>
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https://doi.org/10.21105/joss.01292</unstructured_citation>
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<journal_title>ACM Transactions on Mathematical
Software</journal_title>
<issue>2</issue>
<volume>40</volume>
<doi>10.1145/2566630</doi>
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<cYear>2014</cYear>
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B., Rognes, M. E., &amp; Wells, G. N. (2014). Unified form language: A
domain-specific language for weak formulations of partial differential
equations. ACM Transactions on Mathematical Software, 40(2).
https://doi.org/10.1145/2566630</unstructured_citation>
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Trilinos Project website.
https://web.archive.org/web/20240228185301/https://trilinos.github.io/rol.html</unstructured_citation>
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<article_title>Optimal design of photonic nanojets under
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<author>Alghamdi</author>
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<cYear>2022</cYear>
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uncertainty.
https://doi.org/10.48550/arXiv.2209.02454</unstructured_citation>
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(2018). Optimization of PDEs with uncertain inputs. In H. Antil, D. P.
Kouri, M.-D. Lacasse, &amp; D. Ridzal (Eds.), Frontiers in
PDE-constrained optimization (pp. 41–81). Springer New York.
https://doi.org/10.1007/978-1-4939-8636-1_2</unstructured_citation>
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G. von. (2017). Rapid optimization library. Sandia National
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https://trilinos.github.io/pdfs/ROL.pdf</unstructured_citation>
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<doi>10.21314/jor.2000.038</doi>
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2(3), 21–41.
https://doi.org/10.21314/jor.2000.038</unstructured_citation>
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