{"id":25267389,"url":"https://github.com/thchang/thchang-refs","last_synced_at":"2026-02-22T00:41:10.900Z","repository":{"id":276693920,"uuid":"929997212","full_name":"thchang/thchang-refs","owner":"thchang","description":"Bib information and reading notes for papers I've been reading","archived":false,"fork":false,"pushed_at":"2025-11-20T23:50:50.000Z","size":4190,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-11-21T00:10:06.151Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/thchang.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-02-09T21:37:45.000Z","updated_at":"2025-11-20T23:50:53.000Z","dependencies_parsed_at":null,"dependency_job_id":"c5fdbae6-0ee1-42e3-bf37-e579038b28f6","html_url":"https://github.com/thchang/thchang-refs","commit_stats":null,"previous_names":["thchang/thchang-refs"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/thchang/thchang-refs","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thchang%2Fthchang-refs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thchang%2Fthchang-refs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thchang%2Fthchang-refs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thchang%2Fthchang-refs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thchang","download_url":"https://codeload.github.com/thchang/thchang-refs/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thchang%2Fthchang-refs/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29701143,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-21T23:35:04.139Z","status":"ssl_error","status_checked_at":"2026-02-21T23:35:03.832Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-02-12T09:36:00.250Z","updated_at":"2026-02-22T00:41:10.884Z","avatar_url":"https://github.com/thchang.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Tyler's References and Reading List\n\nBib information and reading notes for papers I've read.\n\nReading list is a yaml file generated using my personal [bibmgr tool](https://github.com/thchang/bib-manager).\n\nList of topics (may be some overlap between topics):\n\n * [AI](#AI)\n * [SciML](#SciML)\n * [optimization](#optimization)\n * [HPC](#HPC)\n * [software](#software)\n * [computational geometry](#computational-geometry)\n * [design of experiments](#design-of-experiments)\n * [quantum computing](#quantum-computing)\n\n## AI\n\n * [Alhyari et al., 2019. A Deep Learning Framework to Predict Routability for FPGA Circuit Placement](https://ieeexplore.ieee.org/document/8892143/)\n * [Ba et al., 2016. Layer Normalization](https://arxiv.org/abs/1607.06450v1)\n * [Bengio et al., 2013. Representation Learning: A Review and New Perspectives](http://ieeexplore.ieee.org/document/6472238/)\n * [Boyd et al., 2004. Convex optimization](https://stanford.edu/~boyd/cvxbook/)\n * [Bradbury et al., 2018. JAX: composable transformations of Python+NumPy programs](http://github.com/google/jax)\n * [Brockman et al., 2016. OpenAI Gym](https://github.com/openai/gym)\n * [Bronstein et al., 2021. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://doi.com/10.48550/arXiv.2104.13478)\n * [Brown et al., 2020. Language Models are Few-Shot Learners](https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf)\n * [Chen et al., 2016. XGBoost: A Scalable Tree Boosting System](https://dl.acm.org/doi/10.1145/2939672.2939785)\n * [Chen et al., 2021. Evaluating large language models trained on code](https://doi.com/10.48550/arXiv.2107.03374)\n * [Chiang et al., 2024. Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://openreview.net/forum?id=3MW8GKNyzI)\n * [Chollet et al., 2015. Keras](https://keras.io)\n * [Dash et al., 2024. Optimizing Distributed Training on Frontier for Large Language Models](https://ieeexplore.ieee.org/document/10528939/)\n * [Dauphin et al., 2014. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization](https://proceedings.neurips.cc/paper_files/paper/2014/file/04192426585542c54b96ba14445be996-Paper.pdf)\n * [Deng et al., 2009. ImageNet: A large-scale hierarchical image database](https://ieeexplore.ieee.org/document/5206848/)\n * [Devlin et al., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423/)\n * [Dhariwal et al., 2017. OpenAI Baselines](https://github.com/openai/baselines)\n * [Dubois et al., 2024. Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://doi.com/10.48550/arXiv.2404.04475)\n * [Duchi et al., 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://jmlr.org/papers/v12/duchi11a.html)\n * [Fawzi et al., 2022. Discovering faster matrix multiplication algorithms with reinforcement learning](https://www.nature.com/articles/s41586-022-05172-4)\n * [Frostig et al., 2018. Compiling machine learning programs via high-level tracing](https://mlsys.org/Conferences/doc/2018/146.pdf)\n * [Fukushima, 1975. Cognitron: A self-organizing multilayered neural network](http://link.springer.com/10.1007/BF00342633)\n * [Gao et al., 2023. Scaling Laws for Reward Model Overoptimization](https://proceedings.mlr.press/v202/gao23h.html)\n * [Geman et al., 1992. Neural networks and the bias/variance dilemma](https://direct.mit.edu/neco/article/4/1/1-58/5624)\n * [Goh, 2017. Why Momentum Really Works](http://distill.pub/2017/momentum)\n * [Gorban et al., 2017. Stochastic separation theorems](https://linkinghub.elsevier.com/retrieve/pii/S0893608017301776)\n * [Grattafiori et al., 2024. The LLaMA 3 herd of models](https://arxiv.org/abs/2407.21783)\n * [Graves, 2014. Generating Sequences With Recurrent Neural Networks](https://arxiv.org/abs/1308.0850)\n * [Guo et al., 2025. DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning](https://doi.com/10.48550/arXiv.2501.12948)\n * [He et al., 2016. Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385)\n * [Heek et al., 2024. Flax: A neural network library and ecosystem for JAX](http://github.com/google/flax)\n * [Hinton et al., 1983. Optimal perceptual inference](https://citeseerx.ist.psu.edu/document?repid=rep1\u0026type=pdf\u0026doi=b89e9f0cef5ace08946a7c07bf7284854c418445)\n * [Hochreiter et al., 1997. Long Short-Term Memory](https://direct.mit.edu/neco/article/9/8/1735-1780/6109)\n * [Hof, 2015. Google Tries to Make Machine Learning a Little More Human](https://www.technologyreview.com/2015/11/05/165175/google-tries-to-make-machine-learning-a-little-more-human)\n * [Ilyas et al., 2019. Adversarial Examples Are Not Bugs, They Are Features](https://proceedings.neurips.cc/paper/2019/file/e2c420d928d4bf8ce0ff2ec19b371514-Paper.pdf)\n * [Jain et al., 2022. tiktoken](https://github.com/openai/tiktoken)\n * [Jain et al., 2024. LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://doi.com/10.48550/arXiv.2403.07974)\n * [Jordan, 1986. Serial order: a parallel distributed processing approach](https://www.osti.gov/biblio/6910294)\n * [Kingma et al., 2014. Auto-encoding variational Bayes](https://arxiv.org/abs/1312.6114)\n * [Kingma et al., 2015. Adam: A method for stochastic optimization](https://arxiv.org/abs/1412.6980)\n * [Krizhevsky, 2009. Learning multiple layers of features from tiny images](https://www.cs.utoronto.ca/~kriz/learning-features-2009-TR.pdf)\n * [Krizhevsky et al., 2012. ImageNet Classification with Deep Convolutional Neural Networks](https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf)\n * [Lai, 2003. Stochastic approximation](https://projecteuclid.org/journals/annals-of-statistics/volume-31/issue-2/Stochastic-approximation-invited-paper/10.1214/aos/1051027873.full)\n * [LeCun et al., 1989. Backpropagation Applied to Handwritten Zip Code Recognition](https://direct.mit.edu/neco/article/1/4/541-551/5515)\n * [LeCun et al., 1995. Convolutional networks for images, speech, and time series](https://www.cs.utoronto.ca/~bonner/courses/2016s/csc321/readings/Convolutional%20networks%20for%20images,%20speech,%20and%20time%20series.pdf)\n * [LeCun et al., 1998. Gradient-based learning applied to document recognition](http://ieeexplore.ieee.org/document/726791/)\n * [Liu et al., 2024. DeepSeek-V3 technical report](https://doi.com/10.48550/arXiv.2412.19437)\n * [Lux et al., 2020. Analytic test functions for generalizable evaluation of convex optimization techniques](https://ieeexplore.ieee.org/document/9368254/)\n * [Mikolov et al., 2013. Efficient estimation of word representations in vector space](https://openreview.net/forum?id=idpCdOWtqXd60\u0026noteld=C8Vn84fq)\n * [Mikolov et al., 2013. Linguistic regularities in continuous space word representations](https://aclanthology.org/N13-1090.pdf)\n * [Nair et al., 2010. Rectified linear units improve restricted Boltzmann machines](https://www.cs.toronto.edu/~fritz/absps/reluICML.pdf)\n * [Nesterov, 1983. A method for solving the convex programming problem with convergence rate $O(1/k^2)$](https://www.mathnet.ru/php/archive.phtml?wshow=paper\u0026jrnid=dan\u0026paperid=46009\u0026option_lang=eng)\n * [Ng, 2004. Feature selection, L1 vs. L2 regularization, and rotational invariance](https://doi.org/10.1145/1015330.1015435)\n * [Park et al., 1991. Universal approximation using radial-basis-function networks](https://direct.mit.edu/neco/article/3/2/246-257/5580)\n * [Paszke et al., 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://proceedings.neurips.cc/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf)\n * [Pedregosa et al., 2011. Scikit-learn: Machine learning in Python](https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf)\n * [Radford et al., 2018. Improving language understanding by generative pre-training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)\n * [Radford et al., 2019. Language models are unsupervised multitask learners](https://storage.prod.researchhub.com/uploads/papers/2020/06/01/language-models.pdf)\n * [Recht et al., 2019. Do ImageNet Classifiers Generalize to ImageNet?](https://proceedings.mlr.press/v97/recht19a.html)\n * [Rosenblatt, 1958. The perceptron: a probabilistic model for information storage and organization in the brain.](https://doi.apa.org/doi/10.1037/h0042519)\n * [Rumelhart et al., 1985. Learning internal representations by error propagation](https://www.cs.toronto.edu/~hinton/absps/pdp8.pdf)\n * [Rumelhart et al., 1986. Learning representations by back-propagating errors](https://www.nature.com/articles/323533a0)\n * [Schulman et al., 2017. Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347)\n * [Sennrich et al., 2016. Neural Machine Translation of Rare Words with Subword Units](https://aclanthology.org/P16-1162/)\n * [Shao et al., 2024. DeepSeekMath: Pushing the limits of mathematical reasoning in open language models](https://doi.com/10.48550/arXiv.2402.03300)\n * [Shwartz-Ziv et al., 2022. Tabular data: Deep learning is not all you need](https://linkinghub.elsevier.com/retrieve/pii/S1566253521002360)\n * [Silver et al., 2016. Mastering the game of Go with deep neural networks and tree search](https://www.nature.com/articles/nature16961)\n * [Sohl-Dickstein et al., 2015. Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://proceedings.mlr.press/v37/sohl-dickstein15.html)\n * [Srivastava et al., 2014. Dropout: a simple way to prevent neural networks from overfitting](https://jmlr.org/papers/v15/srivastava14a.html)\n * [Tarnawski et al., 2020. Efficient algorithms for device placement of DNN graph operators](https://proceedings.neurips.cc/paper_files/paper/2020/file/b14680dec683e744ada1f2fe08614086-Paper.pdf)\n * [Team et al., 2023. Gemini: a family of highly capable multimodal models](https://doi.com/10.48550/arXiv.2312.11805)\n * [Touvron et al., 2023. LLaMA: Open and efficient foundation language models](https://doi.com/10.48550/arXiv.2302.13971)\n * [Touvron et al., 2023. LLaMA~2: Open foundation and fine-tuned chat models](https://arxiv.org/abs/2307.09288)\n * [Vaswani et al., 2017. Attention is all you need](https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf)\n * [Vinyals et al., 2019. Grandmaster level in StarCraft II using multi-agent reinforcement learning](https://doi.org/10.1038/s41586-019-1724-z)\n * [Wang et al., 2024. Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations](https://aclanthology.org/2024.acl-long.510)\n * [Wang et al., 2024. MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://proceedings.neurips.cc/paper_files/paper/2024/file/ad236edc564f3e3156e1b2feafb99a24-Paper-Datasets_and_Benchmarks_Track.pdf)\n * [Wei et al., 2022. Chain-of-thought prompting elicits reasoning in large language models](https://openreview.net/forum?id=_VjQlMeSB_J)\n * [Weinan, 2020. Machine learning and computational mathematics](https://global-sci.com/article/79736/machine-learning-and-computational-mathematics)\n * [Yann, 1998. The MNIST database of handwritten digits](yann.lecun.com/exdb/mnist)\n * [Yu et al., 2019. Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets](https://dl.acm.org/doi/10.1145/3316781.3317876)\n * [Zhang et al., 2017. Understanding deep learning requires rethinking generalization](https://openreview.net/forum?id=Sy8gdB9xx)\n * [Zhou et al., 2023. Instruction-Following Evaluation for Large Language Models](https://arxiv.org/abs/2311.07911)\n * [Ziegler et al., 2019. Fine-Tuning Language Models from Human Preferences](https://arxiv.org/abs/1909.08593)\n\n## SciML\n\n * [Agrawal et al., 2019. Differentiable Convex Optimization Layers](https://proceedings.neurips.cc/paper_files/paper/2019/file/9ce3c52fc54362e22053399d3181c638-Paper.pdf)\n * [Akiba et al., 2019. Optuna: A next-generation hyperparameter optimization framework](https://dl.acm.org/doi/10.1145/3292500.3330701)\n * [Amos et al., 2017. OptNet: Differentiable Optimization as a Layer in Neural Networks](https://proceedings.mlr.press/v70/amos17a.html)\n * [Amos, 2023. Tutorial on Amortized Optimization](https://doi.org/10.1561/2200000102)\n * [Applegate et al., 2021. Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient](https://proceedings.neurips.cc/paper_files/paper/2021/file/a8fbbd3b11424ce032ba813493d95ad7-Paper.pdf)\n * [Balandat et al., 2020. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization](https://proceedings.neurips.cc/paper/2020/file/f5b1b89d98b7286673128a5fb112cb9a-Paper.pdf)\n * [Balaprakash et al., 2018. DeepHyper: Asynchronous hyperparameter search for deep neural networks](https://ieeexplore.ieee.org/document/8638041)\n * [Ball et al., 1992. On the sensitivity of radial basis interpolation to minimal data separation distance](http://link.springer.com/10.1007/BF01203461)\n * [Bambade et al., 2022. PROX-QP: Yet another Quadratic Programming Solver for Robotics and beyond](https://hal.inria.fr/hal-03683733)\n * [Belkin et al., 2018. Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate](https://proceedings.neurips.cc/paper/2018/hash/e22312179bf43e61576081a2f250f845-Abstract.html)\n * [Belkin, 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation](https://www.cambridge.org/core/product/identifier/S0962492921000039/type/journal_article)\n * [Bengio et al., 2013. Representation Learning: A Review and New Perspectives](http://ieeexplore.ieee.org/document/6472238/)\n * [Blondel et al., 2022. Efficient and Modular Implicit Differentiation](https://proceedings.neurips.cc/paper_files/paper/2022/file/228b9279ecf9bbafe582406850c57115-Paper-Conference.pdf)\n * [Bollapragada et al., 2020. Optimization and supervised machine learning methods for fitting numerical physics models without derivatives](https://iopscience.iop.org/article/10.1088/1361-6471/abd009)\n * [Boyd et al., 2004. Convex optimization](https://stanford.edu/~boyd/cvxbook/)\n * [Bradbury et al., 2018. JAX: composable transformations of Python+NumPy programs](http://github.com/google/jax)\n * [Brockman et al., 2016. OpenAI Gym](https://github.com/openai/gym)\n * [Bronstein et al., 2021. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://doi.com/10.48550/arXiv.2104.13478)\n * [Buhmann, 2000. Radial basis functions](https://www.cambridge.org/core/product/identifier/S0962492900000015/type/journal_article)\n * [Chang et al., 2020. Algorithm 1012: DELAUNAYSPARSE: Interpolation via a Sparse Subset of the Delaunay Triangulation in Medium to High Dimensions](https://dl.acm.org/doi/10.1145/3422818)\n * [Chang, 2020. Mathematical Software for Multiobjective Optimization Problems](https://vtechworks.lib.vt.edu/handle/10919/98915)\n * [Chang et al., 2025. Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning](https://linkinghub.elsevier.com/retrieve/pii/S0021999125000099)\n * [Chen et al., 2004. Optimal Delaunay triangulations](https://www.math.uci.edu/~chenlong/Papers/Chen.L%3BXu.J2004.pdf)\n * [Chen et al., 2016. XGBoost: A Scalable Tree Boosting System](https://dl.acm.org/doi/10.1145/2939672.2939785)\n * [Cheney et al., 2009. A Course in Approximation Theory](http://www.ams.org/gsm/101)\n * [Chollet et al., 2015. Keras](https://keras.io)\n * [Christianson et al., 2022. Traditional kriging versus modern Gaussian processes for large-scale mining data](https://arxiv.org/abs/2207.10138)\n * [Conn et al., 2008. Geometry of interpolation sets in derivative free optimization](http://link.springer.com/10.1007/s10107-006-0073-5)\n * [Daigavane et al., 2021. Understanding Convolutions on Graphs](https://distill.pub/2021/understanding-gnns)\n * [Dauphin et al., 2014. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization](https://proceedings.neurips.cc/paper_files/paper/2014/file/04192426585542c54b96ba14445be996-Paper.pdf)\n * [de Boor, 1978. A Practical Guide to Splines](https://www.researchgate.net/publication/200744645_A_Practical_Guide_to_Spline)\n * [Delaunay, 1934. Sur la sph\\'ere vide](https://www.mathnet.ru/php/archive.phtml?wshow=paper\u0026jrnid=im\u0026paperid=4937\u0026option_lang=eng)\n * [De Ryck et al., 2022. Generic bounds on the approximation error for physics-informed (and) operator learning](https://proceedings.neurips.cc/paper_files/paper/2022/file/46f0114c06524debc60ef2a72769f7a9-Paper-Conference.pdf)\n * [Devlin et al., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423/)\n * [Diamond et al., 2016. CVXPY: A Python-embedded modeling language for convex optimization](http://jmlr.org/papers/v17/15-408.html)\n * [Domahidi et al., 2013. ECOS: An SOCP solver for embedded systems](https://ieeexplore.ieee.org/document/6669541)\n * [Duchi et al., 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://jmlr.org/papers/v12/duchi11a.html)\n * [Egele et al., 2022. AutoDEUQ: Automated deep ensemble with uncertainty quantification](https://ieeexplore.ieee.org/document/9956231/)\n * [Farhan et al., 2020. Reinforcement Learning in AnyLogic Simulation Models: A Guiding Example using Pathmind](https://ieeexplore.ieee.org/document/9383916)\n * [Fawzi et al., 2022. Discovering faster matrix multiplication algorithms with reinforcement learning](https://www.nature.com/articles/s41586-022-05172-4)\n * [Frostig et al., 2018. Compiling machine learning programs via high-level tracing](https://mlsys.org/Conferences/doc/2018/146.pdf)\n * [Fukushima, 1975. Cognitron: A self-organizing multilayered neural network](http://link.springer.com/10.1007/BF00342633)\n * [Garg et al., 2023. SF-SFD: Stochastic optimization of Fourier coefficients for space-filling designs](https://ieeexplore.ieee.org/document/10408245/)\n * [Garnett, 2023. Bayesian Optimization](https://bayesoptbook.com)\n * [Geman et al., 1992. Neural networks and the bias/variance dilemma](https://direct.mit.edu/neco/article/4/1/1-58/5624)\n * [Gillette et al., 2022. Data-driven geometric scale detection via Delaunay interpolation](https://arxiv.org/html/2203.05685v2)\n * [Gillette et al., 2024. Algorithm 1049: The Delaunay Density Diagnostic](https://doi.org/10.1145/3700134)\n * [Goh, 2017. Why Momentum Really Works](http://distill.pub/2017/momentum)\n * [Golub et al., 2013. Matrix computations](https://www.press.jhu.edu/books/title/10678/matrix-computations)\n * [Gorban et al., 2017. Stochastic separation theorems](https://linkinghub.elsevier.com/retrieve/pii/S0893608017301776)\n * [Gramacy et al., 2012. Cases for the nugget in modeling computer experiments](http://link.springer.com/10.1007/s11222-010-9224-x)\n * [Graves, 2014. Generating Sequences With Recurrent Neural Networks](https://arxiv.org/abs/1308.0850)\n * [G{\\\"u}hring et al., 2020. Error bounds for approximations with deep ReLU neural networks in $W^s,p$ norms](https://www.worldscientific.com/doi/abs/10.1142/S0219530519410021)\n * [Harris et al., 2020. Array programming with NumPy](https://www.nature.com/articles/s41586-020-2649-2)\n * [Heek et al., 2024. Flax: A neural network library and ecosystem for JAX](http://github.com/google/flax)\n * [Hinton et al., 1983. Optimal perceptual inference](https://citeseerx.ist.psu.edu/document?repid=rep1\u0026type=pdf\u0026doi=b89e9f0cef5ace08946a7c07bf7284854c418445)\n * [Hochreiter et al., 1997. Long Short-Term Memory](https://direct.mit.edu/neco/article/9/8/1735-1780/6109)\n * [Hof, 2015. Google Tries to Make Machine Learning a Little More Human](https://www.technologyreview.com/2015/11/05/165175/google-tries-to-make-machine-learning-a-little-more-human)\n * [Huangfu et al., 2018. Parallelizing the dual revised simplex method](http://link.springer.com/10.1007/s12532-017-0130-5)\n * [Ilyas et al., 2019. Adversarial Examples Are Not Bugs, They Are Features](https://proceedings.neurips.cc/paper/2019/file/e2c420d928d4bf8ce0ff2ec19b371514-Paper.pdf)\n * [Jordan, 1986. Serial order: a parallel distributed processing approach](https://www.osti.gov/biblio/6910294)\n * [Jrad et al., 2019. Self-Learning, Adaptive Software for Aerospace Engineering Applications: Example of Oblique Shocks in Supersonic Flow](https://arc.aiaa.org/doi/10.2514/6.2019-1704)\n * [Kandasamy et al., 2020. 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Global Convergence of Radial Basis Function Trust Region Derivative-Free Algorithms](http://epubs.siam.org/doi/10.1137/09074927X)\n * [Wild, 2017. Solving Derivative-Free Nonlinear Least Squares Problems with POUNDERS](http://www.mcs.anl.gov/papers/P5120-0414.pdf)\n * [Wong et al., 2016. Hypervolume-Based DIRECT for Multi-Objective Optimisation](https://dl.acm.org/doi/10.1145/2908961.2931702)\n * [Wu et al., 2025. ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales](https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.8322)\n * [Wächter et al., 2006. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming](http://link.springer.com/10.1007/s10107-004-0559-y)\n * [Xin et al., 2018. Interactive Multiobjective Optimization: A Review of the State-of-the-Art](https://ieeexplore.ieee.org/document/8412189)\n * [Yang et al., 2019. Multi-Objective Bayesian Global Optimization Using Expected Hypervolume Improvement Gradient](https://linkinghub.elsevier.com/retrieve/pii/S2210650217307861)\n * [Yang et al., 2019. A multi-point mechanism of expected hypervolume improvement for parallel multi-objective Bayesian global optimization](https://dl.acm.org/doi/10.1145/3321707.3321784)\n * [Yuan et al., 2023. Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials](https://www.nature.com/articles/s41524-023-00967-z)\n * [Yukish, 2004. Algorithms to identify Pareto points in multi-dimensional data sets](https://etda.libraries.psu.edu/catalog/6336)\n * [Zhang et al., 2010. A Derivative-Free Algorithm for Least-Squares Minimization](http://epubs.siam.org/doi/10.1137/09075531X)\n * [Zhang et al., 2012. On the Local Convergence of a Derivative-Free Algorithm for Least-Squares Minimization](http://link.springer.com/10.1007/s10589-010-9367-x)\n * [Zhang, 2023. PRIMA: Reference Implementation for Powell's Methods with Modernization and Amelioration](http://www.libprima.net)\n * [Zhao et al., 2018. Multiobjective Optimization of Composite Flying-wings with SpaRibs and Multiple Control Surfaces](https://arc.aiaa.org/doi/10.2514/6.2018-3424)\n * [Zhu et al., 1997. Algorithm 778: L-BFGS-B: Fortran Subroutines for Large-Scale Bound-Constrained Optimization](https://dl.acm.org/doi/10.1145/279232.279236)\n * [Zitzler et al., 2001. SPEA2: Improving the strength Pareto evolutionary algorithm](https://doi.com/10.3929/ethz-a-004284029)\n\n## HPC\n\n * [Adams et al., 2022. Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 User's Manual](https://dakota.sandia.gov/sites/default/files/docs/6.16.0/Users-6.16.0.pdf)\n * [Akiba et al., 2019. Optuna: A next-generation hyperparameter optimization framework](https://dl.acm.org/doi/10.1145/3292500.3330701)\n * [Anderson et al., 1999. LAPACK Users' Guide](https://netlib.org/lapack/lug/)\n * [Audet et al., 2022. Algorithm 1027: NOMAD Version 4: Nonlinear Optimization with the MADS Algorithm](https://dl.acm.org/doi/10.1145/3544489)\n * [Balandat et al., 2020. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization](https://proceedings.neurips.cc/paper/2020/file/f5b1b89d98b7286673128a5fb112cb9a-Paper.pdf)\n * [Balaprakash et al., 2018. DeepHyper: Asynchronous hyperparameter search for deep neural networks](https://ieeexplore.ieee.org/document/8638041)\n * [Balay et al., 2022. PETSc/TAO Users Manual](https://petsc.org/release/docs/manual/manual.pdf)\n * [Barba-González et al., 2018. jMetalSP: A framework for dynamic multi-objective big data optimization](https://linkinghub.elsevier.com/retrieve/pii/S1568494617302557)\n * [Barker et al., 2022. Introducing the FAIR Principles for research software](https://www.nature.com/articles/s41597-022-01710-x)\n * [Biedron et al., 2019. FUN3D Manual: 13.6](https://fun3d.larc.nasa.gov/papers/FUN3D_Manual-13.6.pdf)\n * [Blackford et al., 1997. ScaLAPACK Users' Guide](https://www.netlib.org/scalapack/)\n * [Blondel et al., 2022. Efficient and Modular Implicit Differentiation](https://proceedings.neurips.cc/paper_files/paper/2022/file/228b9279ecf9bbafe582406850c57115-Paper-Conference.pdf)\n * [Bradbury et al., 2018. JAX: composable transformations of Python+NumPy programs](http://github.com/google/jax)\n * [Cameron et al., 2019. MOANA: Modeling and analyzing I/O variability in parallel system experimental design](https://ieeexplore.ieee.org/document/8631172)\n * [Cao et al., 2017. On the performance variation in modern storage stacks](https://www.usenix.org/conference/fast17/technical-sessions/presentation/cao)\n * [Capps et al., 2016. IOzone Filesystem Benchmark](www.iozone.org)\n * [Chandy et al., 1997. A parallel circuit-partitioned algorithm for timing driven cell placement](http://ieeexplore.ieee.org/document/628930/)\n * [Chang et al., 2018. Predicting system performance by interpolation using a high-dimensional Delaunay triangulation](https://par.nsf.gov/servlets/purl/10111451)\n * [Chang et al., 2020. Algorithm 1012: DELAUNAYSPARSE: Interpolation via a Sparse Subset of the Delaunay Triangulation in Medium to High Dimensions](https://dl.acm.org/doi/10.1145/3422818)\n * [Chang et al., 2020. Managing computationally expensive blackbox multiobjective optimization problems using libEnsemble](https://dl.acm.org/doi/abs/10.5555/3408207.3408245)\n * [Chang, 2020. Mathematical Software for Multiobjective Optimization Problems](https://vtechworks.lib.vt.edu/handle/10919/98915)\n * [Chang et al., 2020. Multiobjective optimization of the variability of the high-performance LINPACK solver](https://ieeexplore.ieee.org/document/9383875)\n * [Chang et al., 2022. Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems](https://dl.acm.org/doi/10.1145/3529258)\n * [Chang et al., 2023. ParMOO: A Python library for parallel multiobjective simulation optimization](https://joss.theoj.org/papers/10.21105/joss.04468)\n * [Chang et al., 2024. ParMOO: Python library for parallel multiobjective simulation optimization](https://parmoo.readthedocs.io/en/latest)\n * [Chang et al., 2025. Designing a Framework for Solving Multiobjective Simulation Optimization Problems](https://pubsonline.informs.org/doi/10.1287/ijoc.2023.0250)\n * [Chang et al., 2025. Repository for ``Designing a Framework for Solving Multiobjective Simulation Optimization Problems''](https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0250)\n * [Chard et al., 2020. funcX: A federated function serving fabric for science](https://dl.acm.org/doi/10.1145/3369583.3392683)\n * [Chen et al., 2016. XGBoost: A Scalable Tree Boosting System](https://dl.acm.org/doi/10.1145/2939672.2939785)\n * [Chen et al., 2023. An Integrated Multi-Physics Optimization Framework for Particle Accelerator Design](https://doi.com/10.48550/arXiv.2311.09415)\n * [Dantzig, 1998. Linear Programming and Extensions](https://d1wqtxts1xzle7.cloudfront.net/56278680/Libro_Linear_Programming_George_Dantzig-libre.pdf?1523307505=\u0026response-content-disposition=inline%3B+filename%3DLinear_Programming_and_Extensions.pdf\u0026Expires=1746491165\u0026Signature=WQRD07CTKkhpfjxG1R6Kb2tSq0cRnDUia1ETKdgTQX2wbUxpA2p7ZGudVpOpbsKgUZzsKL-U3CddGBaVVSTr~TSLwPadmYe8xHRVZ4KqyB~ms5zyu08vntJ0V-pRNY0sws9H~ktLJTgoABlZMkoYDA23Dbrh07yQqukyaqHsDuoTEZRzng6AIqN7CXO1KW2M4J~rS-M1mmM3bdTSMAoWPozK7Suea-HJPd7QbCMq2hB0JY5mhhi6nUHa6zIQVmjTCcPPdnX9O4lYgYPQgOBiMlIJ5yhYolhlHKXMA~2-g3rbpe4kqJXIEqICSWPByh72uohGvRJkDgUX-CkBw7FZNA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA)\n * [Dash et al., 2024. Optimizing Distributed Training on Frontier for Large Language Models](https://ieeexplore.ieee.org/document/10528939/)\n * [De et al., 2008. A trace-driven emulation framework to predict scalability of large clusters in presence of OS jitter](http://ieeexplore.ieee.org/document/4663776)\n * [Dean et al., 2008. MapReduce: simplified data processing on large clusters](https://doi.org/10.1145/1327452.1327492)\n * [Dean et al., 2013. The tail at scale](https://research.google/pubs/the-tail-at-scale)\n * [2019. Press Release: U.S.\\ Department of Energy and Intel to deliver first exascale supercomputer](https://www.anl.gov/article/us-department-of-energy-and-intel-to-deliver-first-exascale-supercomputer)\n * [Devlin et al., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423/)\n * [Dongarra et al., 2003. The LINPACK benchmark: past, present, and future](https://onlinelibrary.wiley.com/doi/10.1002/cpe.728)\n * [Dubey et al., 2021. Performance Portability in the Exascale Computing Project: Exploration Through a Panel Series](https://ieeexplore.ieee.org/document/9495114)\n * [Dunlop et al., 2008. On the use of a genetic algorithm in high performance computing benchmark tuning](https://ieeexplore.ieee.org/document/4667550)\n * [Dunning et al., 2017. JuMP: A Modeling Language for Mathematical Optimization](https://epubs.siam.org/doi/10.1137/15M1020575)\n * [Elias et al., 2020. The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT](https://ieeexplore.ieee.org/document/9221078)\n * [Fortin et al., 2012. DEAP: Evolutionary Algorithms Made Easy](https://www.jmlr.org/papers/v13/fortin12a.html)\n * [Forum, 2023. MPI: A Message-Passing Interface Standard](https://www.mpi-forum.org/docs/mpi-4.1/mpi41-report.pdf)\n * [Frostig et al., 2018. Compiling machine learning programs via high-level tracing](https://mlsys.org/Conferences/doc/2018/146.pdf)\n * [Germann, 2021. Co-design in the Exascale Computing Project](https://journals.sagepub.com/doi/10.1177/10943420211059380)\n * [Golub et al., 2013. Matrix computations](https://www.press.jhu.edu/books/title/10678/matrix-computations)\n * [Grattafiori et al., 2024. The LLaMA 3 herd of models](https://arxiv.org/abs/2407.21783)\n * [Gray et al., 2019. OpenMDAO: An open-source framework for multidisciplinary design, analysis, and optimization](http://link.springer.com/10.1007/s00158-019-02211-z)\n * [Hanson et al., 1982. Algorithm 587: Two Algorithms for the Linearly Constrained Least Squares Problem](https://dl.acm.org/doi/10.1145/356004.356010)\n * [Harris et al., 2020. Array programming with NumPy](https://www.nature.com/articles/s41586-020-2649-2)\n * [He et al., 2009. Algorithm 897: VTDIRECT95: Serial and Parallel Codes for the Global Optimization Algorithm DIRECT](https://dl.acm.org/doi/10.1145/1527286.1527291)\n * [He et al., 2009. Performance modeling and analysis of a massively parallel DIRECT -- part 1](https://journals.sagepub.com/doi/10.1177/1094342008098463)\n * [Heek et al., 2024. Flax: A neural network library and ecosystem for JAX](http://github.com/google/flax)\n * [Heroux et al., 2020. Advancing Scientific Productivity through Better Scientific Software: Developer Productivity and Software Sustainability Report](https://www.osti.gov/servlets/purl/1606662)\n * [Hert et al., 2020. dD Convex Hulls and Delaunay Triangulations](https://doc.cgal.org/5.0.2/Manual/packages.html#PkgConvexHullD)\n * [Huang et al., 2019. Cpp-Taskflow: Fast Task-Based Parallel Programming Using Modern C++](https://ieeexplore.ieee.org/document/8821011/)\n * [Huangfu et al., 2018. Parallelizing the dual revised simplex method](http://link.springer.com/10.1007/s12532-017-0130-5)\n * [Hudson et al., 2022. libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations](https://ieeexplore.ieee.org/document/9439163)\n * [Hudson et al., 2023. libEnsemble: A complete Python toolkit for dynamic ensembles of calculations](https://joss.theoj.org/papers/10.21105/joss.06031)\n * [Intel, 2025. oneAPI Threading Building Blocks (oneTBB)](https://uxlfoundation.github.io/oneTBB)\n * [2004. Information technology -- Programming languages -- Fortran -- Part 1: Base Language](https://j3-fortran.org/doc/year/04/04-007.pdf)\n * [2010. Information technology -- Programming languages -- Fortran -- Part 1: Base Language](https://j3-fortran.org/doc/year/10/10-007.pdf)\n * [Kale et al., 1993. CHARM++: a portable concurrent object oriented system based on C++](https://dl.acm.org/doi/10.1145/165854.165874)\n * [Kleen, 2005. A NUMA API for Linux](https://halobates.de/numaapi3.pdf)\n * [Kolonay et al., 2011. Service oriented computing environment (SORCER) for large scale, distributed, dynamic fidelity aeroelastic analysis](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.656.7539)\n * [Kreps et al., 2011. Kafka: A distributed messaging system for log processing](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/09/Kafka.pdf)\n * [Krizhevsky et al., 2012. ImageNet Classification with Deep Convolutional Neural Networks](https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf)\n * [Lasalle et al., 2013. Multi-threaded Graph Partitioning](http://ieeexplore.ieee.org/document/6569814/)\n * [Lavely, 2022. Powering Extreme-Scale HPC with Cerebras Wafer-Scale Accelerators](https://8968533.fs1.hubspotusercontent-na1.net/hubfs/8968533/Powering-Extreme-Scale-HPC-with-Cerebras.pdf)\n * [Le Digabel, 2011. Algorithm 909: NOMAD: Nonlinear Optimization with the MADS Algorithm](https://dl.acm.org/doi/10.1145/1916461.1916468)\n * [Liu et al., 2024. DeepSeek-V3 technical report](https://doi.com/10.48550/arXiv.2412.19437)\n * [Louw et al., 2021. Using the Graphcore IPU for traditional HPC applications](https://easychair.org/publications/preprint/ztfj)\n * [Luo et al., 2020. Pre-exascale accelerated application development: The ORNL Summit experience](https://ieeexplore.ieee.org/document/8960361)\n * [Lux et al., 2018. Nonparametric distribution models for predicting and managing computational performance variability](https://ieeexplore.ieee.org/document/8478814)\n * [Lux et al., 2018. Predictive modeling of I/O characteristics in high performance computing systems](https://par.nsf.gov/servlets/purl/10111447)\n * [M{\\\"a}rtens et al., 2013. The asynchronous island model and NSGA-II: study of a new migration operator and its performance](https://doi.org/10.1145/2463372.2463516)\n * [Mayes, 2023. PySuperfish](https://github.com/ChristopherMayes/PySuperfish)\n * [Menzel et al., 1987. Users guide for the POISSON/SUPERFISH group of codes](https://www.osti.gov/servlets/purl/10140823)\n * [Mills et al., 2021. Toward performance-portable PETSc for GPU-based exascale systems](https://linkinghub.elsevier.com/retrieve/pii/S016781912100079X)\n * [Munson et al., 2015. TAO 3.5 Users Manual](https://www.mcs.anl.gov/petsc/petsc-3.5.4/docs/tao_manual.pdf)\n * [Neveu et al., 2023. Comparison of multiobjective optimization methods for the LCLS-II photoinjector](https://linkinghub.elsevier.com/retrieve/pii/S0010465522002855)\n * [NVIDIA Corporation, 2025. cuBLAS API](https://docs.nvidia.com/cuda/pdf/CUBLAS_Library.pdf)\n * [NVIDIA HPC Compilers, 2025. CUDA Fortran programming guide](https://docs.nvidia.com/hpc-sdk/archive/23.3/pdf/hpc233cudaforug.pdf)\n * [OpenMP, 2015. OpenMP Application Programming Interface](https://www.openmp.org/wp-content/uploads/openmp-4.5.pdf)\n * [Paszke et al., 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://proceedings.neurips.cc/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf)\n * [Petitet et al., 2018. HPL -- A Portable Implementation of the High-Performance Linpack Benchmark for Distributed-Memory Computers](https://www.netlib.org/benchmark/hpl/)\n * [Petrini et al., 2003. The case of the missing supercomputer performance: Achieving optimal performance on the 8,192 processors of ASCI Q](https://dl.acm.org/doi/10.1145/1048935.1050204)\n * [Raghunath et al., 2017. Global deterministic and stochastic optimization in a service oriented architecture](http://dl.acm.org/citation.cfm?id=3108103)\n * [Shroff et al., 1992. Adaptive condition estimation for rank-one updates of QR factorizations](http://epubs.siam.org/doi/10.1137/0613077)\n * [Slepicka, 2020. Poisson Superfish via Docker](https://github.com/hhslepicka/docker-poisson-superfish-nobin)\n * [Stall et al., 2019. Make scientific data FAIR](https://www.nature.com/articles/d41586-019-01720-7)\n * [Strohmaier et al., 2019. The Top 500 List](https://www.top500.org)\n * [Tavares et al., 2022. Parallel Strategies for Direct Multisearch](https://link.springer.com/10.1007/s11075-022-01364-1)\n * [Trott et al., 2022. Kokkos 3: Programming Model Extensions for the Exascale Era](https://ieeexplore.ieee.org/document/9485033/)\n * [Vandevender et al., 1982. The SLATEC Mathematical Subroutine Library](https://dl.acm.org/doi/10.1145/1057594.1057595)\n * [Virtanen et al., 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python](https://www.nature.com/articles/s41592-019-0686-2)\n * [Voss et al., 2019. TBB on NUMA Architectures](https://doi.org/10.1007/978-1-4842-4398-5_20)\n * [Wang et al., 2023. Design strategies and approximation methods for high-performance computing variability management](https://www.tandfonline.com/doi/full/10.1080/00224065.2022.2035285)\n * [Whaley et al., 2001. Automated empirical optimizations of software and the ATLAS project](https://linkinghub.elsevier.com/retrieve/pii/S0167819100000879)\n * [Wu et al., 2025. ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales](https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.8322)\n * [Xiong et al., 2023. DREAMPlaceFPGA-MP: An Open-Source GPU-Accelerated Macro Placer for Modern FPGAs with Cascade Shapes and Region Constraints](https://arxiv.org/abs/2311.08582)\n * [Xu et al., 2020. Modeling I/O performance variability in high-performance computing systems using mixture distributions](https://linkinghub.elsevier.com/retrieve/pii/S0743731519302746)\n * [Zhang, 2023. PRIMA: Reference Implementation for Powell's Methods with Modernization and Amelioration](http://www.libprima.net)\n * [Zhu et al., 1997. Algorithm 778: L-BFGS-B: Fortran Subroutines for Large-Scale Bound-Constrained Optimization](https://dl.acm.org/doi/10.1145/279232.279236)\n\n## software\n\n * [Adams et al., 2022. Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 User's Manual](https://dakota.sandia.gov/sites/default/files/docs/6.16.0/Users-6.16.0.pdf)\n * [Agrawal et al., 2019. Differentiable Convex Optimization Layers](https://proceedings.neurips.cc/paper_files/paper/2019/file/9ce3c52fc54362e22053399d3181c638-Paper.pdf)\n * [Akiba et al., 2019. Optuna: A next-generation hyperparameter optimization framework](https://dl.acm.org/doi/10.1145/3292500.3330701)\n * [AMD Vivado Developers, 2024. Vivado Design Suite User Guide](https://docs.amd.com/r/2024.1-English/ug893-vivado-ide)\n * [Amos et al., 2020. Algorithm 1007: QNSTOP: Quasi-Newton algorithm for stochastic optimization](https://dl.acm.org/doi/10.1145/3374219)\n * [Andersen et al., 2000. The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm](https://link.springer.com/chapter/10.1007/978-1-4757-3216-0_8)\n * [Anderson et al., 1999. LAPACK Users' Guide](https://netlib.org/lapack/lug/)\n * [Andr\\'{e}s-Thi\\'{o} et al., 2025. solar: A solar thermal power plant simulator for blackbox optimization benchmarking](https://link.springer.com/10.1007/s11081-024-09952-x)\n * [Applegate et al., 2021. Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient](https://proceedings.neurips.cc/paper_files/paper/2021/file/a8fbbd3b11424ce032ba813493d95ad7-Paper.pdf)\n * [Audet et al., 2008. 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