From a1cf3cc09f6ddc6f69d167bc3b773c5d8b7fc534 Mon Sep 17 00:00:00 2001 From: dcluo Date: Fri, 26 Jul 2024 10:57:42 -0700 Subject: [PATCH] Added reference for CVaR --- paper/paper.bib | 12 ++++++++++++ paper/paper.md | 2 +- 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/paper/paper.bib b/paper/paper.bib index 8171ccc..6172010 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -217,3 +217,15 @@ @techreport{KouriRidzalWinckel17 month = {01}, url = {https://trilinos.github.io/pdfs/ROL.pdf} } + +@Article{RockafellarUryasev00, + author = {R. Tyrrell Rockafellar and Stanislav Uryasev}, + journal = {The Journal of Risk}, + title = {Optimization of conditional value-at-risk}, + year = {2000}, + number = {3}, + pages = {21--41}, + volume = {2}, + doi = {10.21314/jor.2000.038}, + publisher = {Infopro Digital Services Limited}, +} diff --git a/paper/paper.md b/paper/paper.md index 50ac90b..0779ebc 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -46,7 +46,7 @@ SOUPy is a Python library for solving PDE-constrained optimization problems with The optimization problem is defined by a risk measure over a given quantity of interest (QoI), which is present as either an optimization objective or constraint (as in chance-constrained optimization). Specific attention is given to the case where the uncertain parameters are formally infinite dimensional (e.g. Gaussian random fields). The software allows users to supply the underlying PDE model, quantity of interest, and penalty terms, -and provides implementations for commonly used risk measures, including expectation, variance, and superquantile/conditional value-at-risk (CVaR), as well as derivative-based optimizers. +and provides implementations for commonly used risk measures, including expectation, variance, and superquantile/conditional value-at-risk (CVaR) [@RockafellarUryasev00], as well as derivative-based optimizers. SOUPy leverages FEniCS [@LoggMardalWells12] for the formulation, discretization, and solution of PDEs, and the framework of hIPPYlib [@VillaPetraGhattas18; @VillaPetraGhattas21] for sampling from random fields and automating adjoint-based derivative computation, while also providing interfaces to existing optimization algorithms in SciPy.