https://github.com/thchang/thchang-refs
Bib information and reading notes for papers I've been reading
https://github.com/thchang/thchang-refs
Last synced: 5 months ago
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Bib information and reading notes for papers I've been reading
- Host: GitHub
- URL: https://github.com/thchang/thchang-refs
- Owner: thchang
- Created: 2025-02-09T21:37:45.000Z (over 1 year ago)
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- Last Pushed: 2025-11-20T23:50:50.000Z (8 months ago)
- Last Synced: 2025-11-21T00:10:06.151Z (8 months ago)
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Metadata Files:
- Readme: README.md
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README
# Tyler's References and Reading List
Bib information and reading notes for papers I've read.
Reading list is a yaml file generated using my personal [bibmgr tool](https://github.com/thchang/bib-manager).
List of topics (may be some overlap between topics):
* [AI](#AI)
* [SciML](#SciML)
* [optimization](#optimization)
* [HPC](#HPC)
* [software](#software)
* [computational geometry](#computational-geometry)
* [design of experiments](#design-of-experiments)
* [quantum computing](#quantum-computing)
## AI
* [Alhyari et al., 2019. A Deep Learning Framework to Predict Routability for FPGA Circuit Placement](https://ieeexplore.ieee.org/document/8892143/)
* [Ba et al., 2016. Layer Normalization](https://arxiv.org/abs/1607.06450v1)
* [Bengio et al., 2013. Representation Learning: A Review and New Perspectives](http://ieeexplore.ieee.org/document/6472238/)
* [Boyd et al., 2004. Convex optimization](https://stanford.edu/~boyd/cvxbook/)
* [Bradbury et al., 2018. JAX: composable transformations of Python+NumPy programs](http://github.com/google/jax)
* [Brockman et al., 2016. OpenAI Gym](https://github.com/openai/gym)
* [Bronstein et al., 2021. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://doi.com/10.48550/arXiv.2104.13478)
* [Brown et al., 2020. Language Models are Few-Shot Learners](https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf)
* [Chen et al., 2016. XGBoost: A Scalable Tree Boosting System](https://dl.acm.org/doi/10.1145/2939672.2939785)
* [Chen et al., 2021. Evaluating large language models trained on code](https://doi.com/10.48550/arXiv.2107.03374)
* [Chiang et al., 2024. Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://openreview.net/forum?id=3MW8GKNyzI)
* [Chollet et al., 2015. Keras](https://keras.io)
* [Dash et al., 2024. Optimizing Distributed Training on Frontier for Large Language Models](https://ieeexplore.ieee.org/document/10528939/)
* [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)
* [Deng et al., 2009. ImageNet: A large-scale hierarchical image database](https://ieeexplore.ieee.org/document/5206848/)
* [Devlin et al., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423/)
* [Dhariwal et al., 2017. OpenAI Baselines](https://github.com/openai/baselines)
* [Dubois et al., 2024. Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://doi.com/10.48550/arXiv.2404.04475)
* [Duchi et al., 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://jmlr.org/papers/v12/duchi11a.html)
* [Fawzi et al., 2022. Discovering faster matrix multiplication algorithms with reinforcement learning](https://www.nature.com/articles/s41586-022-05172-4)
* [Frostig et al., 2018. Compiling machine learning programs via high-level tracing](https://mlsys.org/Conferences/doc/2018/146.pdf)
* [Fukushima, 1975. Cognitron: A self-organizing multilayered neural network](http://link.springer.com/10.1007/BF00342633)
* [Gao et al., 2023. Scaling Laws for Reward Model Overoptimization](https://proceedings.mlr.press/v202/gao23h.html)
* [Geman et al., 1992. Neural networks and the bias/variance dilemma](https://direct.mit.edu/neco/article/4/1/1-58/5624)
* [Goh, 2017. Why Momentum Really Works](http://distill.pub/2017/momentum)
* [Gorban et al., 2017. Stochastic separation theorems](https://linkinghub.elsevier.com/retrieve/pii/S0893608017301776)
* [Grattafiori et al., 2024. The LLaMA 3 herd of models](https://arxiv.org/abs/2407.21783)
* [Graves, 2014. Generating Sequences With Recurrent Neural Networks](https://arxiv.org/abs/1308.0850)
* [Guo et al., 2025. DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning](https://doi.com/10.48550/arXiv.2501.12948)
* [He et al., 2016. Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385)
* [Heek et al., 2024. Flax: A neural network library and ecosystem for JAX](http://github.com/google/flax)
* [Hinton et al., 1983. Optimal perceptual inference](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b89e9f0cef5ace08946a7c07bf7284854c418445)
* [Hochreiter et al., 1997. Long Short-Term Memory](https://direct.mit.edu/neco/article/9/8/1735-1780/6109)
* [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)
* [Ilyas et al., 2019. Adversarial Examples Are Not Bugs, They Are Features](https://proceedings.neurips.cc/paper/2019/file/e2c420d928d4bf8ce0ff2ec19b371514-Paper.pdf)
* [Jain et al., 2022. tiktoken](https://github.com/openai/tiktoken)
* [Jain et al., 2024. LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://doi.com/10.48550/arXiv.2403.07974)
* [Jordan, 1986. Serial order: a parallel distributed processing approach](https://www.osti.gov/biblio/6910294)
* [Kingma et al., 2014. Auto-encoding variational Bayes](https://arxiv.org/abs/1312.6114)
* [Kingma et al., 2015. Adam: A method for stochastic optimization](https://arxiv.org/abs/1412.6980)
* [Krizhevsky, 2009. Learning multiple layers of features from tiny images](https://www.cs.utoronto.ca/~kriz/learning-features-2009-TR.pdf)
* [Krizhevsky et al., 2012. ImageNet Classification with Deep Convolutional Neural Networks](https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf)
* [Lai, 2003. Stochastic approximation](https://projecteuclid.org/journals/annals-of-statistics/volume-31/issue-2/Stochastic-approximation-invited-paper/10.1214/aos/1051027873.full)
* [LeCun et al., 1989. Backpropagation Applied to Handwritten Zip Code Recognition](https://direct.mit.edu/neco/article/1/4/541-551/5515)
* [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)
* [LeCun et al., 1998. Gradient-based learning applied to document recognition](http://ieeexplore.ieee.org/document/726791/)
* [Liu et al., 2024. DeepSeek-V3 technical report](https://doi.com/10.48550/arXiv.2412.19437)
* [Lux et al., 2020. Analytic test functions for generalizable evaluation of convex optimization techniques](https://ieeexplore.ieee.org/document/9368254/)
* [Mikolov et al., 2013. Efficient estimation of word representations in vector space](https://openreview.net/forum?id=idpCdOWtqXd60¬eld=C8Vn84fq)
* [Mikolov et al., 2013. Linguistic regularities in continuous space word representations](https://aclanthology.org/N13-1090.pdf)
* [Nair et al., 2010. Rectified linear units improve restricted Boltzmann machines](https://www.cs.toronto.edu/~fritz/absps/reluICML.pdf)
* [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&jrnid=dan&paperid=46009&option_lang=eng)
* [Ng, 2004. Feature selection, L1 vs. L2 regularization, and rotational invariance](https://doi.org/10.1145/1015330.1015435)
* [Park et al., 1991. Universal approximation using radial-basis-function networks](https://direct.mit.edu/neco/article/3/2/246-257/5580)
* [Paszke et al., 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://proceedings.neurips.cc/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf)
* [Pedregosa et al., 2011. Scikit-learn: Machine learning in Python](https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf)
* [Radford et al., 2018. Improving language understanding by generative pre-training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)
* [Radford et al., 2019. Language models are unsupervised multitask learners](https://storage.prod.researchhub.com/uploads/papers/2020/06/01/language-models.pdf)
* [Recht et al., 2019. Do ImageNet Classifiers Generalize to ImageNet?](https://proceedings.mlr.press/v97/recht19a.html)
* [Rosenblatt, 1958. The perceptron: a probabilistic model for information storage and organization in the brain.](https://doi.apa.org/doi/10.1037/h0042519)
* [Rumelhart et al., 1985. Learning internal representations by error propagation](https://www.cs.toronto.edu/~hinton/absps/pdp8.pdf)
* [Rumelhart et al., 1986. Learning representations by back-propagating errors](https://www.nature.com/articles/323533a0)
* [Schulman et al., 2017. Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347)
* [Sennrich et al., 2016. Neural Machine Translation of Rare Words with Subword Units](https://aclanthology.org/P16-1162/)
* [Shao et al., 2024. DeepSeekMath: Pushing the limits of mathematical reasoning in open language models](https://doi.com/10.48550/arXiv.2402.03300)
* [Shwartz-Ziv et al., 2022. Tabular data: Deep learning is not all you need](https://linkinghub.elsevier.com/retrieve/pii/S1566253521002360)
* [Silver et al., 2016. Mastering the game of Go with deep neural networks and tree search](https://www.nature.com/articles/nature16961)
* [Sohl-Dickstein et al., 2015. Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://proceedings.mlr.press/v37/sohl-dickstein15.html)
* [Srivastava et al., 2014. Dropout: a simple way to prevent neural networks from overfitting](https://jmlr.org/papers/v15/srivastava14a.html)
* [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)
* [Team et al., 2023. Gemini: a family of highly capable multimodal models](https://doi.com/10.48550/arXiv.2312.11805)
* [Touvron et al., 2023. LLaMA: Open and efficient foundation language models](https://doi.com/10.48550/arXiv.2302.13971)
* [Touvron et al., 2023. LLaMA~2: Open foundation and fine-tuned chat models](https://arxiv.org/abs/2307.09288)
* [Vaswani et al., 2017. Attention is all you need](https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf)
* [Vinyals et al., 2019. Grandmaster level in StarCraft II using multi-agent reinforcement learning](https://doi.org/10.1038/s41586-019-1724-z)
* [Wang et al., 2024. Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations](https://aclanthology.org/2024.acl-long.510)
* [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)
* [Wei et al., 2022. Chain-of-thought prompting elicits reasoning in large language models](https://openreview.net/forum?id=_VjQlMeSB_J)
* [Weinan, 2020. Machine learning and computational mathematics](https://global-sci.com/article/79736/machine-learning-and-computational-mathematics)
* [Yann, 1998. The MNIST database of handwritten digits](yann.lecun.com/exdb/mnist)
* [Yu et al., 2019. Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets](https://dl.acm.org/doi/10.1145/3316781.3317876)
* [Zhang et al., 2017. Understanding deep learning requires rethinking generalization](https://openreview.net/forum?id=Sy8gdB9xx)
* [Zhou et al., 2023. Instruction-Following Evaluation for Large Language Models](https://arxiv.org/abs/2311.07911)
* [Ziegler et al., 2019. Fine-Tuning Language Models from Human Preferences](https://arxiv.org/abs/1909.08593)
## SciML
* [Agrawal et al., 2019. Differentiable Convex Optimization Layers](https://proceedings.neurips.cc/paper_files/paper/2019/file/9ce3c52fc54362e22053399d3181c638-Paper.pdf)
* [Akiba et al., 2019. Optuna: A next-generation hyperparameter optimization framework](https://dl.acm.org/doi/10.1145/3292500.3330701)
* [Amos et al., 2017. OptNet: Differentiable Optimization as a Layer in Neural Networks](https://proceedings.mlr.press/v70/amos17a.html)
* [Amos, 2023. Tutorial on Amortized Optimization](https://doi.org/10.1561/2200000102)
* [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)
* [Balandat et al., 2020. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization](https://proceedings.neurips.cc/paper/2020/file/f5b1b89d98b7286673128a5fb112cb9a-Paper.pdf)
* [Balaprakash et al., 2018. DeepHyper: Asynchronous hyperparameter search for deep neural networks](https://ieeexplore.ieee.org/document/8638041)
* [Ball et al., 1992. On the sensitivity of radial basis interpolation to minimal data separation distance](http://link.springer.com/10.1007/BF01203461)
* [Bambade et al., 2022. PROX-QP: Yet another Quadratic Programming Solver for Robotics and beyond](https://hal.inria.fr/hal-03683733)
* [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)
* [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)
* [Bengio et al., 2013. Representation Learning: A Review and New Perspectives](http://ieeexplore.ieee.org/document/6472238/)
* [Blondel et al., 2022. Efficient and Modular Implicit Differentiation](https://proceedings.neurips.cc/paper_files/paper/2022/file/228b9279ecf9bbafe582406850c57115-Paper-Conference.pdf)
* [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)
* [Boyd et al., 2004. Convex optimization](https://stanford.edu/~boyd/cvxbook/)
* [Bradbury et al., 2018. JAX: composable transformations of Python+NumPy programs](http://github.com/google/jax)
* [Brockman et al., 2016. OpenAI Gym](https://github.com/openai/gym)
* [Bronstein et al., 2021. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://doi.com/10.48550/arXiv.2104.13478)
* [Buhmann, 2000. Radial basis functions](https://www.cambridge.org/core/product/identifier/S0962492900000015/type/journal_article)
* [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)
* [Chang, 2020. Mathematical Software for Multiobjective Optimization Problems](https://vtechworks.lib.vt.edu/handle/10919/98915)
* [Chang et al., 2025. Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning](https://linkinghub.elsevier.com/retrieve/pii/S0021999125000099)
* [Chen et al., 2004. Optimal Delaunay triangulations](https://www.math.uci.edu/~chenlong/Papers/Chen.L%3BXu.J2004.pdf)
* [Chen et al., 2016. XGBoost: A Scalable Tree Boosting System](https://dl.acm.org/doi/10.1145/2939672.2939785)
* [Cheney et al., 2009. A Course in Approximation Theory](http://www.ams.org/gsm/101)
* [Chollet et al., 2015. Keras](https://keras.io)
* [Christianson et al., 2022. Traditional kriging versus modern Gaussian processes for large-scale mining data](https://arxiv.org/abs/2207.10138)
* [Conn et al., 2008. Geometry of interpolation sets in derivative free optimization](http://link.springer.com/10.1007/s10107-006-0073-5)
* [Daigavane et al., 2021. Understanding Convolutions on Graphs](https://distill.pub/2021/understanding-gnns)
* [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)
* [de Boor, 1978. A Practical Guide to Splines](https://www.researchgate.net/publication/200744645_A_Practical_Guide_to_Spline)
* [Delaunay, 1934. Sur la sph\'ere vide](https://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=im&paperid=4937&option_lang=eng)
* [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)
* [Devlin et al., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423/)
* [Diamond et al., 2016. CVXPY: A Python-embedded modeling language for convex optimization](http://jmlr.org/papers/v17/15-408.html)
* [Domahidi et al., 2013. ECOS: An SOCP solver for embedded systems](https://ieeexplore.ieee.org/document/6669541)
* [Duchi et al., 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://jmlr.org/papers/v12/duchi11a.html)
* [Egele et al., 2022. AutoDEUQ: Automated deep ensemble with uncertainty quantification](https://ieeexplore.ieee.org/document/9956231/)
* [Farhan et al., 2020. Reinforcement Learning in AnyLogic Simulation Models: A Guiding Example using Pathmind](https://ieeexplore.ieee.org/document/9383916)
* [Fawzi et al., 2022. Discovering faster matrix multiplication algorithms with reinforcement learning](https://www.nature.com/articles/s41586-022-05172-4)
* [Frostig et al., 2018. Compiling machine learning programs via high-level tracing](https://mlsys.org/Conferences/doc/2018/146.pdf)
* [Fukushima, 1975. Cognitron: A self-organizing multilayered neural network](http://link.springer.com/10.1007/BF00342633)
* [Garg et al., 2023. SF-SFD: Stochastic optimization of Fourier coefficients for space-filling designs](https://ieeexplore.ieee.org/document/10408245/)
* [Garnett, 2023. Bayesian Optimization](https://bayesoptbook.com)
* [Geman et al., 1992. Neural networks and the bias/variance dilemma](https://direct.mit.edu/neco/article/4/1/1-58/5624)
* [Gillette et al., 2022. Data-driven geometric scale detection via Delaunay interpolation](https://arxiv.org/html/2203.05685v2)
* [Gillette et al., 2024. Algorithm 1049: The Delaunay Density Diagnostic](https://doi.org/10.1145/3700134)
* [Goh, 2017. Why Momentum Really Works](http://distill.pub/2017/momentum)
* [Golub et al., 2013. Matrix computations](https://www.press.jhu.edu/books/title/10678/matrix-computations)
* [Gorban et al., 2017. Stochastic separation theorems](https://linkinghub.elsevier.com/retrieve/pii/S0893608017301776)
* [Gramacy et al., 2012. Cases for the nugget in modeling computer experiments](http://link.springer.com/10.1007/s11222-010-9224-x)
* [Graves, 2014. Generating Sequences With Recurrent Neural Networks](https://arxiv.org/abs/1308.0850)
* [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)
* [Harris et al., 2020. Array programming with NumPy](https://www.nature.com/articles/s41586-020-2649-2)
* [Heek et al., 2024. Flax: A neural network library and ecosystem for JAX](http://github.com/google/flax)
* [Hinton et al., 1983. Optimal perceptual inference](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b89e9f0cef5ace08946a7c07bf7284854c418445)
* [Hochreiter et al., 1997. Long Short-Term Memory](https://direct.mit.edu/neco/article/9/8/1735-1780/6109)
* [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)
* [Huangfu et al., 2018. Parallelizing the dual revised simplex method](http://link.springer.com/10.1007/s12532-017-0130-5)
* [Ilyas et al., 2019. Adversarial Examples Are Not Bugs, They Are Features](https://proceedings.neurips.cc/paper/2019/file/e2c420d928d4bf8ce0ff2ec19b371514-Paper.pdf)
* [Jordan, 1986. Serial order: a parallel distributed processing approach](https://www.osti.gov/biblio/6910294)
* [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)
* [Kandasamy et al., 2020. Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly](http://jmlr.org/papers/v21/18-223.html)
* [Karpatne et al., 2017. Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data](http://ieeexplore.ieee.org/document/7959606)
* [Kimeldorf et al., 1971. Some results on Tchebycheffian spline functions](https://pages.stat.wisc.edu/~wahba/ftp1/oldie/kw71.pdf)
* [Kingma et al., 2014. Auto-encoding variational Bayes](https://arxiv.org/abs/1312.6114)
* [Kingma et al., 2015. Adam: A method for stochastic optimization](https://arxiv.org/abs/1412.6980)
* [Kovachki et al., 2021. Neural Operator: Learning Maps Between Function Spaces](https://www.jmlr.org/papers/volume24/21-1524/21-1524.pdf)
* [Krizhevsky et al., 2012. ImageNet Classification with Deep Convolutional Neural Networks](https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf)
* [Lai, 2003. Stochastic approximation](https://projecteuclid.org/journals/annals-of-statistics/volume-31/issue-2/Stochastic-approximation-invited-paper/10.1214/aos/1051027873.full)
* [Lax, 2002. Functional analysis](https://books.google.com/books?hl=en&lr=&id=18VqDwAAQBAJ&oi=fnd&pg=PR17&ots=8FU4i_jAff&sig=Lh4GrUCrS_gt-HKqHSq4X7BaxMs#v=onepage&q&f=false)
* [LeCun et al., 1989. Backpropagation Applied to Handwritten Zip Code Recognition](https://direct.mit.edu/neco/article/1/4/541-551/5515)
* [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)
* [LeCun et al., 1998. Gradient-based learning applied to document recognition](http://ieeexplore.ieee.org/document/726791/)
* [Lindauer et al., 2022. SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](http://jmlr.org/papers/v23/21-0888.html)
* [Liu et al., 2019. Nonparametric functional approximation with Delaunay triangulation learner](https://ieeexplore.ieee.org/document/8944414)
* [Lundberg et al., 2017. A Unified Approach to Interpreting Model Predictions](http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf)
* [Lux et al., 2018. Nonparametric distribution models for predicting and managing computational performance variability](https://ieeexplore.ieee.org/document/8478814)
* [Lux et al., 2018. Predictive modeling of I/O characteristics in high performance computing systems](https://par.nsf.gov/servlets/purl/10111447)
* [Lux et al., 2020. Analytic test functions for generalizable evaluation of convex optimization techniques](https://ieeexplore.ieee.org/document/9368254/)
* [Lux et al., 2021. Interpolation of sparse high-dimensional data](https://link.springer.com/10.1007/s11075-020-01040-2)
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## optimization
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* [Amos et al., 2020. Algorithm 1007: QNSTOP: Quasi-Newton algorithm for stochastic optimization](https://dl.acm.org/doi/10.1145/3374219)
* [Amos, 2023. Tutorial on Amortized Optimization](https://doi.org/10.1561/2200000102)
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* [Audet et al., 2010. A mesh adaptive direct search algorithm for multiobjective optimization](https://linkinghub.elsevier.com/retrieve/pii/S0377221709008601)
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* [Audet et al., 2021. Performance indicators in multiobjective optimization](https://www.sciencedirect.com/science/article/pii/S0377221720309620)
* [Audet et al., 2021. Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates](https://doi.org/10.1007/s10589-020-00249-0)
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* [Bringmann et al., 2013. Approximation quality of the hypervolume indicator](https://linkinghub.elsevier.com/retrieve/pii/S0004370212001178)
* [Byrd et al., 2006. Knitro: An Integrated Package for Nonlinear Optimization](https://link.springer.com/chapter/10.1007/0-387-30065-1_4)
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* [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)
* [Chang et al., 2020. Managing computationally expensive blackbox multiobjective optimization problems using libEnsemble](https://dl.acm.org/doi/abs/10.5555/3408207.3408245)
* [Chang, 2020. Mathematical Software for Multiobjective Optimization Problems](https://vtechworks.lib.vt.edu/handle/10919/98915)
* [Chang et al., 2020. Multiobjective optimization of the variability of the high-performance LINPACK solver](https://ieeexplore.ieee.org/document/9383875)
* [Chang et al., 2022. Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems](https://dl.acm.org/doi/10.1145/3529258)
* [Chang et al., 2023. A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps](https://openreview.net/forum?id=8KJS7RPjMqG)
* [Chang et al., 2023. ParMOO: A Python library for parallel multiobjective simulation optimization](https://joss.theoj.org/papers/10.21105/joss.04468)
* [Chang et al., 2024. ParMOO: Python library for parallel multiobjective simulation optimization](https://parmoo.readthedocs.io/en/latest)
* [Chang et al., 2024. Remark on Algorithm 1012: Computing projections with large data sets](https://dl.acm.org/doi/10.1145/3656581)
* [Chang et al., 2025. Designing a Framework for Solving Multiobjective Simulation Optimization Problems](https://pubsonline.informs.org/doi/10.1287/ijoc.2023.0250)
* [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)
* [Chen et al., 2017. FPGA placement and routing](http://ieeexplore.ieee.org/document/8203878/)
* [Chen et al., 2023. An Integrated Multi-Physics Optimization Framework for Particle Accelerator Design](https://doi.com/10.48550/arXiv.2311.09415)
* [Chugh, 2020. Scalarizing functions in Bayesian multiobjective optimization](https://ieeexplore.ieee.org/document/9185706/)
* [Cocchi et al., 2018. An implicit filtering algorithm for derivative-free multiobjective optimization with box constraints](http://link.springer.com/10.1007/s10589-017-9953-2)
* [Cocchi et al., 2020. An augmented Lagrangian algorithm for multi-objective optimization](https://link.springer.com/10.1007/s10589-020-00204-z)
* [Conn et al., 1992. LANCELOT: A Fortran Package for Large-Scale Nonlinear Optimization (Release A)](http://link.springer.com/10.1007/978-3-662-12211-2)
* [Conn et al., 2008. Geometry of interpolation sets in derivative free optimization](http://link.springer.com/10.1007/s10107-006-0073-5)
* [Conn et al., 2009. Introduction to derivative-free optimization](http://epubs.siam.org/doi/book/10.1137/1.9780898718768)
* [Cooper et al., 2020. PyMOSO: Software for multi-objective simulation optimization with R-PERLE and R-MinRLE](http://pubsonline.informs.org/doi/10.1287/ijoc.2019.0902)
* [Costa et al., 2018. RBFOpt: an open-source library for black-box optimization with costly function evaluations](http://link.springer.com/10.1007/s12532-018-0144-7)
* [Cristescu et al., 2015. Surrogate-based multiobjective optimization: ParEGO update and test](https://www.cs.bham.ac.uk/~jdk/UKCI-2015.pdf)
* [Cust\'odio et al., 2011. Direct Multisearch for Multiobjective Optimization](http://epubs.siam.org/doi/10.1137/10079731X)
* [Cust{\'{o}}dio et al., 2018. MultiGLODS: global and local multiobjective optimization using direct search](http://link.springer.com/10.1007/s10898-018-0618-1)
* [Dandurand et al., 2016. Quadratic scalarization for decomposed multiobjective optimization](http://link.springer.com/10.1007/s00291-016-0453-z)
* [Dantzig, 1998. Linear Programming and Extensions](https://d1wqtxts1xzle7.cloudfront.net/56278680/Libro_Linear_Programming_George_Dantzig-libre.pdf?1523307505=&response-content-disposition=inline%3B+filename%3DLinear_Programming_and_Extensions.pdf&Expires=1746491165&Signature=WQRD07CTKkhpfjxG1R6Kb2tSq0cRnDUia1ETKdgTQX2wbUxpA2p7ZGudVpOpbsKgUZzsKL-U3CddGBaVVSTr~TSLwPadmYe8xHRVZ4KqyB~ms5zyu08vntJ0V-pRNY0sws9H~ktLJTgoABlZMkoYDA23Dbrh07yQqukyaqHsDuoTEZRzng6AIqN7CXO1KW2M4J~rS-M1mmM3bdTSMAoWPozK7Suea-HJPd7QbCMq2hB0JY5mhhi6nUHa6zIQVmjTCcPPdnX9O4lYgYPQgOBiMlIJ5yhYolhlHKXMA~2-g3rbpe4kqJXIEqICSWPByh72uohGvRJkDgUX-CkBw7FZNA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA)
* [Das et al., 1998. Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems](http://epubs.siam.org/doi/10.1137/S1052623496307510)
* [Dash et al., 2024. Optimizing Distributed Training on Frontier for Large Language Models](https://ieeexplore.ieee.org/document/10528939/)
* [Datta et al., 2016. A surrogate-assisted evolution strategy for constrained multi-objective optimization](https://linkinghub.elsevier.com/retrieve/pii/S0957417416301452)
* [Daulton et al., 2020. Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://proceedings.neurips.cc/paper/2020/file/6fec24eac8f18ed793f5eaad3dd7977c-Paper.pdf)
* [Daulton et al., 2021. Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement](https://proceedings.neurips.cc/paper/2021/file/11704817e347269b7254e744b5e22dac-Paper.pdf)
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* [Deb et al., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II](http://ieeexplore.ieee.org/document/996017)
* [Deb et al., 2002. Scalable multi-objective optimization test problems](http://ieeexplore.ieee.org/document/1007032)
* [Deb et al., 2013. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints](http://ieeexplore.ieee.org/document/6600851)
* [Deshpande et al., 2011. Data driven surrogate-based optimization in the problem solving environment WBCSim](http://link.springer.com/10.1007/s00366-010-0192-8)
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* [Diamond et al., 2016. CVXPY: A Python-embedded modeling language for convex optimization](http://jmlr.org/papers/v17/15-408.html)
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* [Dong et al., 2020. NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr)
* [Duchi et al., 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://jmlr.org/papers/v12/duchi11a.html)
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* [Dunning et al., 2017. JuMP: A Modeling Language for Mathematical Optimization](https://epubs.siam.org/doi/10.1137/15M1020575)
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* [Klee et al., 1972. How good is the simplex algorithm?](https://en.wikipedia.org/wiki/Klee%E2%80%93Minty_cube)
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## HPC
* [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)
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* [Balandat et al., 2020. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization](https://proceedings.neurips.cc/paper/2020/file/f5b1b89d98b7286673128a5fb112cb9a-Paper.pdf)
* [Balaprakash et al., 2018. DeepHyper: Asynchronous hyperparameter search for deep neural networks](https://ieeexplore.ieee.org/document/8638041)
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* [Barba-González et al., 2018. jMetalSP: A framework for dynamic multi-objective big data optimization](https://linkinghub.elsevier.com/retrieve/pii/S1568494617302557)
* [Barker et al., 2022. Introducing the FAIR Principles for research software](https://www.nature.com/articles/s41597-022-01710-x)
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* [Blondel et al., 2022. Efficient and Modular Implicit Differentiation](https://proceedings.neurips.cc/paper_files/paper/2022/file/228b9279ecf9bbafe582406850c57115-Paper-Conference.pdf)
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* [Cameron et al., 2019. MOANA: Modeling and analyzing I/O variability in parallel system experimental design](https://ieeexplore.ieee.org/document/8631172)
* [Cao et al., 2017. On the performance variation in modern storage stacks](https://www.usenix.org/conference/fast17/technical-sessions/presentation/cao)
* [Capps et al., 2016. IOzone Filesystem Benchmark](www.iozone.org)
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* [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)
* [Chang et al., 2020. Managing computationally expensive blackbox multiobjective optimization problems using libEnsemble](https://dl.acm.org/doi/abs/10.5555/3408207.3408245)
* [Chang, 2020. Mathematical Software for Multiobjective Optimization Problems](https://vtechworks.lib.vt.edu/handle/10919/98915)
* [Chang et al., 2020. Multiobjective optimization of the variability of the high-performance LINPACK solver](https://ieeexplore.ieee.org/document/9383875)
* [Chang et al., 2022. Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems](https://dl.acm.org/doi/10.1145/3529258)
* [Chang et al., 2023. ParMOO: A Python library for parallel multiobjective simulation optimization](https://joss.theoj.org/papers/10.21105/joss.04468)
* [Chang et al., 2024. ParMOO: Python library for parallel multiobjective simulation optimization](https://parmoo.readthedocs.io/en/latest)
* [Chang et al., 2025. Designing a Framework for Solving Multiobjective Simulation Optimization Problems](https://pubsonline.informs.org/doi/10.1287/ijoc.2023.0250)
* [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)
* [Chard et al., 2020. funcX: A federated function serving fabric for science](https://dl.acm.org/doi/10.1145/3369583.3392683)
* [Chen et al., 2016. XGBoost: A Scalable Tree Boosting System](https://dl.acm.org/doi/10.1145/2939672.2939785)
* [Chen et al., 2023. An Integrated Multi-Physics Optimization Framework for Particle Accelerator Design](https://doi.com/10.48550/arXiv.2311.09415)
* [Dantzig, 1998. Linear Programming and Extensions](https://d1wqtxts1xzle7.cloudfront.net/56278680/Libro_Linear_Programming_George_Dantzig-libre.pdf?1523307505=&response-content-disposition=inline%3B+filename%3DLinear_Programming_and_Extensions.pdf&Expires=1746491165&Signature=WQRD07CTKkhpfjxG1R6Kb2tSq0cRnDUia1ETKdgTQX2wbUxpA2p7ZGudVpOpbsKgUZzsKL-U3CddGBaVVSTr~TSLwPadmYe8xHRVZ4KqyB~ms5zyu08vntJ0V-pRNY0sws9H~ktLJTgoABlZMkoYDA23Dbrh07yQqukyaqHsDuoTEZRzng6AIqN7CXO1KW2M4J~rS-M1mmM3bdTSMAoWPozK7Suea-HJPd7QbCMq2hB0JY5mhhi6nUHa6zIQVmjTCcPPdnX9O4lYgYPQgOBiMlIJ5yhYolhlHKXMA~2-g3rbpe4kqJXIEqICSWPByh72uohGvRJkDgUX-CkBw7FZNA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA)
* [Dash et al., 2024. Optimizing Distributed Training on Frontier for Large Language Models](https://ieeexplore.ieee.org/document/10528939/)
* [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)
* [Dean et al., 2008. MapReduce: simplified data processing on large clusters](https://doi.org/10.1145/1327452.1327492)
* [Dean et al., 2013. The tail at scale](https://research.google/pubs/the-tail-at-scale)
* [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)
* [Devlin et al., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423/)
* [Dongarra et al., 2003. The LINPACK benchmark: past, present, and future](https://onlinelibrary.wiley.com/doi/10.1002/cpe.728)
* [Dubey et al., 2021. Performance Portability in the Exascale Computing Project: Exploration Through a Panel Series](https://ieeexplore.ieee.org/document/9495114)
* [Dunlop et al., 2008. On the use of a genetic algorithm in high performance computing benchmark tuning](https://ieeexplore.ieee.org/document/4667550)
* [Dunning et al., 2017. JuMP: A Modeling Language for Mathematical Optimization](https://epubs.siam.org/doi/10.1137/15M1020575)
* [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)
* [Fortin et al., 2012. DEAP: Evolutionary Algorithms Made Easy](https://www.jmlr.org/papers/v13/fortin12a.html)
* [Forum, 2023. MPI: A Message-Passing Interface Standard](https://www.mpi-forum.org/docs/mpi-4.1/mpi41-report.pdf)
* [Frostig et al., 2018. Compiling machine learning programs via high-level tracing](https://mlsys.org/Conferences/doc/2018/146.pdf)
* [Germann, 2021. Co-design in the Exascale Computing Project](https://journals.sagepub.com/doi/10.1177/10943420211059380)
* [Golub et al., 2013. Matrix computations](https://www.press.jhu.edu/books/title/10678/matrix-computations)
* [Grattafiori et al., 2024. The LLaMA 3 herd of models](https://arxiv.org/abs/2407.21783)
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* [Hanson et al., 1982. Algorithm 587: Two Algorithms for the Linearly Constrained Least Squares Problem](https://dl.acm.org/doi/10.1145/356004.356010)
* [Harris et al., 2020. Array programming with NumPy](https://www.nature.com/articles/s41586-020-2649-2)
* [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)
* [He et al., 2009. Performance modeling and analysis of a massively parallel DIRECT -- part 1](https://journals.sagepub.com/doi/10.1177/1094342008098463)
* [Heek et al., 2024. Flax: A neural network library and ecosystem for JAX](http://github.com/google/flax)
* [Heroux et al., 2020. Advancing Scientific Productivity through Better Scientific Software: Developer Productivity and Software Sustainability Report](https://www.osti.gov/servlets/purl/1606662)
* [Hert et al., 2020. dD Convex Hulls and Delaunay Triangulations](https://doc.cgal.org/5.0.2/Manual/packages.html#PkgConvexHullD)
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* [Hudson et al., 2023. libEnsemble: A complete Python toolkit for dynamic ensembles of calculations](https://joss.theoj.org/papers/10.21105/joss.06031)
* [Intel, 2025. oneAPI Threading Building Blocks (oneTBB)](https://uxlfoundation.github.io/oneTBB)
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* [2010. Information technology -- Programming languages -- Fortran -- Part 1: Base Language](https://j3-fortran.org/doc/year/10/10-007.pdf)
* [Kale et al., 1993. CHARM++: a portable concurrent object oriented system based on C++](https://dl.acm.org/doi/10.1145/165854.165874)
* [Kleen, 2005. A NUMA API for Linux](https://halobates.de/numaapi3.pdf)
* [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)
* [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)
* [Krizhevsky et al., 2012. ImageNet Classification with Deep Convolutional Neural Networks](https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf)
* [Lasalle et al., 2013. Multi-threaded Graph Partitioning](http://ieeexplore.ieee.org/document/6569814/)
* [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)
* [Le Digabel, 2011. Algorithm 909: NOMAD: Nonlinear Optimization with the MADS Algorithm](https://dl.acm.org/doi/10.1145/1916461.1916468)
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* [Mayes, 2023. PySuperfish](https://github.com/ChristopherMayes/PySuperfish)
* [Menzel et al., 1987. Users guide for the POISSON/SUPERFISH group of codes](https://www.osti.gov/servlets/purl/10140823)
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* [NVIDIA HPC Compilers, 2025. CUDA Fortran programming guide](https://docs.nvidia.com/hpc-sdk/archive/23.3/pdf/hpc233cudaforug.pdf)
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## software
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* [Chang, 2020. Mathematical Software for Multiobjective Optimization Problems](https://vtechworks.lib.vt.edu/handle/10919/98915)
* [Chang et al., 2022. Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems](https://dl.acm.org/doi/10.1145/3529258)
* [Chang et al., 2023. ParMOO: A Python library for parallel multiobjective simulation optimization](https://joss.theoj.org/papers/10.21105/joss.04468)
* [Chang et al., 2024. ParMOO: Python library for parallel multiobjective simulation optimization](https://parmoo.readthedocs.io/en/latest)
* [Chang et al., 2025. Designing a Framework for Solving Multiobjective Simulation Optimization Problems](https://pubsonline.informs.org/doi/10.1287/ijoc.2023.0250)
* [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)
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* [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)
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* [Dongarra et al., 2003. The LINPACK benchmark: past, present, and future](https://onlinelibrary.wiley.com/doi/10.1002/cpe.728)
* [Dubey et al., 2021. Performance Portability in the Exascale Computing Project: Exploration Through a Panel Series](https://ieeexplore.ieee.org/document/9495114)
* [Dunning et al., 2017. JuMP: A Modeling Language for Mathematical Optimization](https://epubs.siam.org/doi/10.1137/15M1020575)
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* [Eckman et al., 2023. SimOpt: A Testbed for Simulation-Optimization Experiments](https://pubsonline.informs.org/doi/10.1287/ijoc.2023.1273)
* [Eggensperger et al., 2021. HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO](https://openreview.net/forum?id=1k4rJYEwda-)
* [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)
* [Farhan et al., 2020. Reinforcement Learning in AnyLogic Simulation Models: A Guiding Example using Pathmind](https://ieeexplore.ieee.org/document/9383916)
* [Fletcher, 1993. Resolving degeneracy in quadratic programming](http://link.springer.com/10.1007/BF02023102)
* [Fortin et al., 2012. DEAP: Evolutionary Algorithms Made Easy](https://www.jmlr.org/papers/v13/fortin12a.html)
* [Forum, 2023. MPI: A Message-Passing Interface Standard](https://www.mpi-forum.org/docs/mpi-4.1/mpi41-report.pdf)
* [Fourer et al., 2003. AMPL: A Modeling Language for Mathematical Programming](https://dev.ampl.com/ampl/books/ampl/index.html)
* [Fowkes et al., 2022. PyCUTEst: an open source Python package of optimization test problems](https://joss.theoj.org/papers/10.21105/joss.04377)
* [Fowkes et al., 2023. GALAHAD 4.0: an open source library of Fortran packages with C and Matlab interfaces for continuous optimization](https://doi.org/10.21105/joss.04882)
* [Frostig et al., 2018. Compiling machine learning programs via high-level tracing](https://mlsys.org/Conferences/doc/2018/146.pdf)
* [Gamma et al., 1995. Design patterns: elements of reusable object-oriented software](https://en.wikipedia.org/wiki/Design_Patterns)
* [Germann, 2021. Co-design in the Exascale Computing Project](https://journals.sagepub.com/doi/10.1177/10943420211059380)
* [Gillette et al., 2024. Algorithm 1049: The Delaunay Density Diagnostic](https://doi.org/10.1145/3700134)
* [Golovin et al., 2017. Google Vizier: A Service for Black-Box Optimization](https://dl.acm.org/doi/10.1145/3097983.3098043)
* [Golub et al., 2013. Matrix computations](https://www.press.jhu.edu/books/title/10678/matrix-computations)
* [Gould et al., 2003. GALAHAD, a library of thread-safe Fortran 90 packages for large-scale nonlinear optimization](https://doi.org/10.1145/962437.962438)
* [Gould et al., 2015. CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization](https://doi.org/10.1007/s10589-014-9687-3)
* [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)
* [Hadka, 2015. Platypus -- multiobjective optimization in Python](https://platypus.readthedocs.io/en/latest)
* [Hanson et al., 1982. Algorithm 587: Two Algorithms for the Linearly Constrained Least Squares Problem](https://dl.acm.org/doi/10.1145/356004.356010)
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