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.

List of topics (may be some overlap between topics):

• AI
• SciML
• optimization
• HPC
• software
• computational geometry
• design of experiments
• quantum computing

AI

• Alhyari et al., 2019. A Deep Learning Framework to Predict Routability for FPGA Circuit Placement
• Ba et al., 2016. Layer Normalization
• Bengio et al., 2013. Representation Learning: A Review and New Perspectives
• Boyd et al., 2004. Convex optimization
• Bradbury et al., 2018. JAX: composable transformations of Python+NumPy programs
• Brockman et al., 2016. OpenAI Gym
• Bronstein et al., 2021. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
• Brown et al., 2020. Language Models are Few-Shot Learners
• Chen et al., 2016. XGBoost: A Scalable Tree Boosting System
• Chen et al., 2021. Evaluating large language models trained on code
• Chiang et al., 2024. Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
• Chollet et al., 2015. Keras
• Dash et al., 2024. Optimizing Distributed Training on Frontier for Large Language Models
• Dauphin et al., 2014. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
• Deng et al., 2009. ImageNet: A large-scale hierarchical image database
• Devlin et al., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Dhariwal et al., 2017. OpenAI Baselines
• Dubois et al., 2024. Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
• Duchi et al., 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
• Fawzi et al., 2022. Discovering faster matrix multiplication algorithms with reinforcement learning
• Frostig et al., 2018. Compiling machine learning programs via high-level tracing
• Fukushima, 1975. Cognitron: A self-organizing multilayered neural network
• Gao et al., 2023. Scaling Laws for Reward Model Overoptimization
• Geman et al., 1992. Neural networks and the bias/variance dilemma
• Goh, 2017. Why Momentum Really Works
• Gorban et al., 2017. Stochastic separation theorems
• Grattafiori et al., 2024. The LLaMA 3 herd of models
• Graves, 2014. Generating Sequences With Recurrent Neural Networks
• Guo et al., 2025. DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning
• He et al., 2016. Deep Residual Learning for Image Recognition
• Heek et al., 2024. Flax: A neural network library and ecosystem for JAX
• Hinton et al., 1983. Optimal perceptual inference
• Hochreiter et al., 1997. Long Short-Term Memory
• Hof, 2015. Google Tries to Make Machine Learning a Little More Human
• Ilyas et al., 2019. Adversarial Examples Are Not Bugs, They Are Features
• Jain et al., 2022. tiktoken
• Jain et al., 2024. LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
• Jordan, 1986. Serial order: a parallel distributed processing approach
• Kingma et al., 2014. Auto-encoding variational Bayes
• Kingma et al., 2015. Adam: A method for stochastic optimization
• Krizhevsky, 2009. Learning multiple layers of features from tiny images
• Krizhevsky et al., 2012. ImageNet Classification with Deep Convolutional Neural Networks
• Lai, 2003. Stochastic approximation
• LeCun et al., 1989. Backpropagation Applied to Handwritten Zip Code Recognition
• LeCun et al., 1995. Convolutional networks for images, speech, and time series
• LeCun et al., 1998. Gradient-based learning applied to document recognition
• Liu et al., 2024. DeepSeek-V3 technical report
• Lux et al., 2020. Analytic test functions for generalizable evaluation of convex optimization techniques
• Mikolov et al., 2013. Efficient estimation of word representations in vector space
• Mikolov et al., 2013. Linguistic regularities in continuous space word representations
• Nair et al., 2010. Rectified linear units improve restricted Boltzmann machines
• Nesterov, 1983. A method for solving the convex programming problem with convergence rate $O(1/k^2)$
• Ng, 2004. Feature selection, L1 vs. L2 regularization, and rotational invariance
• Park et al., 1991. Universal approximation using radial-basis-function networks
• Paszke et al., 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Pedregosa et al., 2011. Scikit-learn: Machine learning in Python
• Radford et al., 2018. Improving language understanding by generative pre-training
• Radford et al., 2019. Language models are unsupervised multitask learners
• Recht et al., 2019. Do ImageNet Classifiers Generalize to ImageNet?
• Rosenblatt, 1958. The perceptron: a probabilistic model for information storage and organization in the brain.
• Rumelhart et al., 1985. Learning internal representations by error propagation
• Rumelhart et al., 1986. Learning representations by back-propagating errors
• Schulman et al., 2017. Proximal Policy Optimization Algorithms
• Sennrich et al., 2016. Neural Machine Translation of Rare Words with Subword Units
• Shao et al., 2024. DeepSeekMath: Pushing the limits of mathematical reasoning in open language models
• Shwartz-Ziv et al., 2022. Tabular data: Deep learning is not all you need
• Silver et al., 2016. Mastering the game of Go with deep neural networks and tree search
• Sohl-Dickstein et al., 2015. Deep Unsupervised Learning using Nonequilibrium Thermodynamics
• Srivastava et al., 2014. Dropout: a simple way to prevent neural networks from overfitting
• Tarnawski et al., 2020. Efficient algorithms for device placement of DNN graph operators
• Team et al., 2023. Gemini: a family of highly capable multimodal models
• Touvron et al., 2023. LLaMA: Open and efficient foundation language models
• Touvron et al., 2023. LLaMA~2: Open foundation and fine-tuned chat models
• Vaswani et al., 2017. Attention is all you need
• Vinyals et al., 2019. Grandmaster level in StarCraft II using multi-agent reinforcement learning
• Wang et al., 2024. Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
• Wang et al., 2024. MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
• Wei et al., 2022. Chain-of-thought prompting elicits reasoning in large language models
• Weinan, 2020. Machine learning and computational mathematics
• Yann, 1998. The MNIST database of handwritten digits
• Yu et al., 2019. Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets
• Zhang et al., 2017. Understanding deep learning requires rethinking generalization
• Zhou et al., 2023. Instruction-Following Evaluation for Large Language Models
• Ziegler et al., 2019. Fine-Tuning Language Models from Human Preferences

SciML

• Agrawal et al., 2019. Differentiable Convex Optimization Layers
• Akiba et al., 2019. Optuna: A next-generation hyperparameter optimization framework
• Amos et al., 2017. OptNet: Differentiable Optimization as a Layer in Neural Networks
• Amos, 2023. Tutorial on Amortized Optimization
• Applegate et al., 2021. Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
• Balandat et al., 2020. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
• Balaprakash et al., 2018. DeepHyper: Asynchronous hyperparameter search for deep neural networks
• Ball et al., 1992. On the sensitivity of radial basis interpolation to minimal data separation distance
• Bambade et al., 2022. PROX-QP: Yet another Quadratic Programming Solver for Robotics and beyond
• Belkin et al., 2018. Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
• Belkin, 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation
• Bengio et al., 2013. Representation Learning: A Review and New Perspectives
• Blondel et al., 2022. Efficient and Modular Implicit Differentiation
• Bollapragada et al., 2020. Optimization and supervised machine learning methods for fitting numerical physics models without derivatives
• Boyd et al., 2004. Convex optimization
• Bradbury et al., 2018. JAX: composable transformations of Python+NumPy programs
• Brockman et al., 2016. OpenAI Gym
• Bronstein et al., 2021. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
• Buhmann, 2000. Radial basis functions
• Chang et al., 2020. Algorithm 1012: DELAUNAYSPARSE: Interpolation via a Sparse Subset of the Delaunay Triangulation in Medium to High Dimensions
• Chang, 2020. Mathematical Software for Multiobjective Optimization Problems
• Chang et al., 2025. Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning
• Chen et al., 2004. Optimal Delaunay triangulations
• Chen et al., 2016. XGBoost: A Scalable Tree Boosting System
• Cheney et al., 2009. A Course in Approximation Theory
• Chollet et al., 2015. Keras
• Christianson et al., 2022. Traditional kriging versus modern Gaussian processes for large-scale mining data
• Conn et al., 2008. Geometry of interpolation sets in derivative free optimization
• Daigavane et al., 2021. Understanding Convolutions on Graphs
• Dauphin et al., 2014. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
• de Boor, 1978. A Practical Guide to Splines
• Delaunay, 1934. Sur la sph'ere vide
• De Ryck et al., 2022. Generic bounds on the approximation error for physics-informed (and) operator learning
• Devlin et al., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Diamond et al., 2016. CVXPY: A Python-embedded modeling language for convex optimization
• Domahidi et al., 2013. ECOS: An SOCP solver for embedded systems
• Duchi et al., 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
• Egele et al., 2022. AutoDEUQ: Automated deep ensemble with uncertainty quantification
• Farhan et al., 2020. Reinforcement Learning in AnyLogic Simulation Models: A Guiding Example using Pathmind
• Fawzi et al., 2022. Discovering faster matrix multiplication algorithms with reinforcement learning
• Frostig et al., 2018. Compiling machine learning programs via high-level tracing
• Fukushima, 1975. Cognitron: A self-organizing multilayered neural network
• Garg et al., 2023. SF-SFD: Stochastic optimization of Fourier coefficients for space-filling designs
• Garnett, 2023. Bayesian Optimization
• Geman et al., 1992. Neural networks and the bias/variance dilemma
• Gillette et al., 2022. Data-driven geometric scale detection via Delaunay interpolation
• Gillette et al., 2024. Algorithm 1049: The Delaunay Density Diagnostic
• Goh, 2017. Why Momentum Really Works
• Golub et al., 2013. Matrix computations
• Gorban et al., 2017. Stochastic separation theorems
• Gramacy et al., 2012. Cases for the nugget in modeling computer experiments
• Graves, 2014. Generating Sequences With Recurrent Neural Networks
• G{"u}hring et al., 2020. Error bounds for approximations with deep ReLU neural networks in $W^s,p$ norms
• Harris et al., 2020. Array programming with NumPy
• Heek et al., 2024. Flax: A neural network library and ecosystem for JAX
• Hinton et al., 1983. Optimal perceptual inference
• Hochreiter et al., 1997. Long Short-Term Memory
• Hof, 2015. Google Tries to Make Machine Learning a Little More Human
• Huangfu et al., 2018. Parallelizing the dual revised simplex method
• Ilyas et al., 2019. Adversarial Examples Are Not Bugs, They Are Features
• Jordan, 1986. Serial order: a parallel distributed processing approach
• Jrad et al., 2019. Self-Learning, Adaptive Software for Aerospace Engineering Applications: Example of Oblique Shocks in Supersonic Flow
• Kandasamy et al., 2020. Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
• Karpatne et al., 2017. Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
• Kimeldorf et al., 1971. Some results on Tchebycheffian spline functions
• Kingma et al., 2014. Auto-encoding variational Bayes
• Kingma et al., 2015. Adam: A method for stochastic optimization
• Kovachki et al., 2021. Neural Operator: Learning Maps Between Function Spaces
• Krizhevsky et al., 2012. ImageNet Classification with Deep Convolutional Neural Networks
• Lai, 2003. Stochastic approximation
• Lax, 2002. Functional analysis
• LeCun et al., 1989. Backpropagation Applied to Handwritten Zip Code Recognition
• LeCun et al., 1995. Convolutional networks for images, speech, and time series
• LeCun et al., 1998. Gradient-based learning applied to document recognition
• Lindauer et al., 2022. SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
• Liu et al., 2019. Nonparametric functional approximation with Delaunay triangulation learner
• Lundberg et al., 2017. A Unified Approach to Interpreting Model Predictions
• Lux et al., 2018. Nonparametric distribution models for predicting and managing computational performance variability
• Lux et al., 2018. Predictive modeling of I/O characteristics in high performance computing systems
• Lux et al., 2020. Analytic test functions for generalizable evaluation of convex optimization techniques
• Lux et al., 2021. Interpolation of sparse high-dimensional data
• Lux et al., 2023. Algorithm 1031: MQSI---Monotone quintic spline interpolation
• MacKay, 1992. A practical Bayesian framework for backpropagation networks
• Manton et al., 2015. A primer on reproducing kernel Hilbert spaces
• Mikolov et al., 2013. Efficient estimation of word representations in vector space
• Mikolov et al., 2013. Linguistic regularities in continuous space word representations
• M"obius, 1827. Der barycentrische Calcul
• Moriwaki et al., 2018. Mordred: a molecular descriptor calculator
• Myers et al., 2016. Response Surface Methodology: Process and Design Optimization Using Designed Experiments
• Nair et al., 2010. Rectified linear units improve restricted Boltzmann machines
• Narayanan et al., 2017. graph2vec: Learning Distributed Representations of Graphs
• Nesterov, 1983. A method for solving the convex programming problem with convergence rate $O(1/k^2)$
• Ng, 2004. Feature selection, L1 vs. L2 regularization, and rotational invariance
• Omohundro, 1990. Geometric learning algorithms
• Park et al., 1991. Universal approximation using radial-basis-function networks
• Paszke et al., 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Patel, 2025. The Marriage of Computable Phenotypes With Machine Learning—A Pathway to Evidence-Based Care for Critically Ill Children
• Pearson, 1901. LIII. On lines and planes of closest fit to systems of points in space
• Pedregosa et al., 2011. Scikit-learn: Machine learning in Python
• Powell, 1994. The uniform convergence of thin plate spline interpolation in two dimensions
• Raissi et al., 2019. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Ranjan et al., 2011. A computationally stable approach to Gaussian process interpolation of deterministic computer simulation data
• Rasmussen et al., 2006. Gaussian processes for machine learning
• Recht et al., 2019. Do ImageNet Classifiers Generalize to ImageNet?
• Regis, 2015. The calculus of simplex gradients
• Rosenblatt, 1958. The perceptron: a probabilistic model for information storage and organization in the brain.
• Roy et al., 2023. Quasi-Monte Carlo Methods in Python
• Rumelhart et al., 1985. Learning internal representations by error propagation
• Rumelhart et al., 1986. Learning representations by back-propagating errors
• Sanchez-Lengeling et al., 2021. A Gentle Introduction to Graph Neural Networks
• Sapsis et al., 2022. Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modelling with Gaussian process regression
• Schaback, 1995. Error estimates and condition numbers for radial basis function interpolation
• Schulman et al., 2017. Proximal Policy Optimization Algorithms
• Sennrich et al., 2016. Neural Machine Translation of Rare Words with Subword Units
• Shapley et al., 1953. A value for n-person games
• Shewchuk, 2002. What is a good linear finite element? Interpolation, conditioning, anisotropy, and quality measures
• Shields et al., 2021. Bayesian reaction optimization as a tool for chemical synthesis
• Shroff et al., 1992. Adaptive condition estimation for rank-one updates of QR factorizations
• Shwartz-Ziv et al., 2022. Tabular data: Deep learning is not all you need
• Silver et al., 2016. Mastering the game of Go with deep neural networks and tree search
• Sohl-Dickstein et al., 2015. Deep Unsupervised Learning using Nonequilibrium Thermodynamics
• Srivastava et al., 2014. Dropout: a simple way to prevent neural networks from overfitting
• Stall et al., 2019. Make scientific data FAIR
• Tavallaee et al., 2009. A detailed analysis of the KDD CUP 99 data set
• Thacker et al., 2010. Algorithm 905: SHEPPACK: Modified Shepard algorithm for interpolation of scattered multivariate data
• Tian et al., 2022. Improving Simulated Annealing Algorithm for FPGA Placement Based on Reinforcement Learning
• van der Maaten et al., 2008. Visualizing Data using t-SNE
• Vaswani et al., 2017. Attention is all you need
• Vinyals et al., 2019. Grandmaster level in StarCraft II using multi-agent reinforcement learning
• Virtanen et al., 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
• Wang et al., 2018. On the numerical rank of radial basis function kernels in high dimensions
• Wathen et al., 2015. On spectral distribution of kernel matrices related to radial basis functions
• Weinan, 2020. Machine learning and computational mathematics
• Yun et al., 2019. Graph transformer networks
• Zhang et al., 2017. Understanding deep learning requires rethinking generalization
• Zhu et al., 1997. Algorithm 778: L-BFGS-B: Fortran Subroutines for Large-Scale Bound-Constrained Optimization

optimization

• 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
• Agrawal et al., 2019. Differentiable Convex Optimization Layers
• Akhtar et al., 2016. Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection
• Akiba et al., 2019. Optuna: A next-generation hyperparameter optimization framework
• Al-Dujaili et al., 2016. Dividing rectangles attack multi-objective optimization
• Al-Dujaili et al., 2016. A MATLAB toolbox for surrogate-assisted multi-objective optimization: A preliminary study
• Alizadeh et al., 2020. Managing computational complexity using surrogate models: a critical review
• Amos et al., 2017. OptNet: Differentiable Optimization as a Layer in Neural Networks
• Amos et al., 2020. Algorithm 1007: QNSTOP: Quasi-Newton algorithm for stochastic optimization
• Amos, 2023. Tutorial on Amortized Optimization
• Andersen et al., 2000. The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm
• Andreani et al., 2022. Using first-order information in direct multisearch for multiobjective optimization
• Andr'{e}s-Thi'{o} et al., 2025. solar: A solar thermal power plant simulator for blackbox optimization benchmarking
• Applegate et al., 2021. Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
• Astudillo et al., 2021. Thinking inside the box: a tutorial on grey-box bayesian optimization
• Audet et al., 2008. Multiobjective optimization through a series of single-objective formulations
• Audet et al., 2008. Nonsmooth optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search
• Audet et al., 2009. A Progressive Barrier for Derivative-Free Nonlinear Programming
• Audet et al., 2010. A mesh adaptive direct search algorithm for multiobjective optimization
• Audet et al., 2017. Derivative-free and blackbox optimization
• Audet et al., 2021. Performance indicators in multiobjective optimization
• Audet et al., 2021. Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates
• Audet et al., 2022. Algorithm 1027: NOMAD Version 4: Nonlinear Optimization with the MADS Algorithm
• Audet et al., 2023. A general mathematical framework for constrained mixed-variable blackbox optimization problems with meta and categorical variables
• Ba et al., 2016. Layer Normalization
• Balandat et al., 2020. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
• Balaprakash et al., 2018. DeepHyper: Asynchronous hyperparameter search for deep neural networks
• Balay et al., 2022. PETSc/TAO Users Manual
• Bambade et al., 2022. PROX-QP: Yet another Quadratic Programming Solver for Robotics and beyond
• Bandyopadhyay et al., 2008. A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
• Bansal et al., 2022. JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search
• Barba-González et al., 2018. jMetalSP: A framework for dynamic multi-objective big data optimization
• Ben{'i}tez-Hidalgo et al., 2019. jMetalPy: A Python framework for multi-objective optimization with metaheuristics
• Bergstra et al., 2011. Algorithms for Hyper-Parameter Optimization
• Berkemeier et al., 2021. Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models
• Beume et al., 2009. On the complexity of computing the hypervolume indicator
• Bian et al., 2010. Towards scalable placement for FPGAs
• Biedron et al., 2019. FUN3D Manual: 13.6
• Bigeon et al., 2020. DMulti-MADS: Mesh adaptive direct multisearch for blackbox multiobjective optimization
• Biscani et al., 2020. A parallel global multiobjective framework for optimization: pagmo
• Blank et al., 2020. pymoo: Multi-Objective Optimization in Python
• Blondel et al., 2022. Efficient and Modular Implicit Differentiation
• Bollapragada et al., 2020. Optimization and supervised machine learning methods for fitting numerical physics models without derivatives
• Bouhlel et al., 2019. A Python surrogate modeling framework with derivatives
• Boyd et al., 2004. Convex optimization
• Bradford et al., 2018. Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm
• Br{'a}s et al., 2020. On the use of polynomial models in multiobjective directional direct search
• Bringmann et al., 2013. Approximation quality of the hypervolume indicator
• Byrd et al., 2006. Knitro: An Integrated Package for Nonlinear Optimization
• Campana et al., 2018. A multi-objective DIRECT algorithm for ship hull optimization
• Caron et al., 2024. qpbenchmark: Benchmark for quadratic programming solvers available in Python
• Chang et al., 2020. Algorithm 1012: DELAUNAYSPARSE: Interpolation via a Sparse Subset of the Delaunay Triangulation in Medium to High Dimensions
• Chang et al., 2020. Managing computationally expensive blackbox multiobjective optimization problems using libEnsemble
• Chang, 2020. Mathematical Software for Multiobjective Optimization Problems
• Chang et al., 2020. Multiobjective optimization of the variability of the high-performance LINPACK solver
• Chang et al., 2022. Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems
• Chang et al., 2023. A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps
• Chang et al., 2023. ParMOO: A Python library for parallel multiobjective simulation optimization
• Chang et al., 2024. ParMOO: Python library for parallel multiobjective simulation optimization
• Chang et al., 2024. Remark on Algorithm 1012: Computing projections with large data sets
• Chang et al., 2025. Designing a Framework for Solving Multiobjective Simulation Optimization Problems
• Chang et al., 2025. Repository for ``Designing a Framework for Solving Multiobjective Simulation Optimization Problems''
• Chen et al., 2017. FPGA placement and routing
• Chen et al., 2023. An Integrated Multi-Physics Optimization Framework for Particle Accelerator Design
• Chugh, 2020. Scalarizing functions in Bayesian multiobjective optimization
• Cocchi et al., 2018. An implicit filtering algorithm for derivative-free multiobjective optimization with box constraints
• Cocchi et al., 2020. An augmented Lagrangian algorithm for multi-objective optimization
• Conn et al., 1992. LANCELOT: A Fortran Package for Large-Scale Nonlinear Optimization (Release A)
• Conn et al., 2008. Geometry of interpolation sets in derivative free optimization
• Conn et al., 2009. Introduction to derivative-free optimization
• Cooper et al., 2020. PyMOSO: Software for multi-objective simulation optimization with R-PERLE and R-MinRLE
• Costa et al., 2018. RBFOpt: an open-source library for black-box optimization with costly function evaluations
• Cristescu et al., 2015. Surrogate-based multiobjective optimization: ParEGO update and test
• Cust'odio et al., 2011. Direct Multisearch for Multiobjective Optimization
• Cust{'{o}}dio et al., 2018. MultiGLODS: global and local multiobjective optimization using direct search
• Dandurand et al., 2016. Quadratic scalarization for decomposed multiobjective optimization
• Dantzig, 1998. Linear Programming and Extensions
• Das et al., 1998. Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems
• Dash et al., 2024. Optimizing Distributed Training on Frontier for Large Language Models
• Datta et al., 2016. A surrogate-assisted evolution strategy for constrained multi-objective optimization
• Daulton et al., 2020. Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
• Daulton et al., 2021. Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
• Dauphin et al., 2014. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
• Deb et al., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II
• Deb et al., 2002. Scalable multi-objective optimization test problems
• Deb et al., 2013. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints
• Deshpande et al., 2011. Data driven surrogate-based optimization in the problem solving environment WBCSim
• Deshpande et al., 2016. Multiobjective optimization using an adaptive weighting scheme
• Diamond et al., 2016. CVXPY: A Python-embedded modeling language for convex optimization
• Domahidi et al., 2013. ECOS: An SOCP solver for embedded systems
• Dong et al., 2020. NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search
• Duchi et al., 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
• Dunlop et al., 2008. On the use of a genetic algorithm in high performance computing benchmark tuning
• Dunning et al., 2017. JuMP: A Modeling Language for Mathematical Optimization
• Durillo et al., 2011. jMetal: A Java framework for multi-objective optimization
• Dzahini et al., 2025. A Class of Sparse Johnson–Lindenstrauss Transforms and Analysis of their Extreme Singular Values
• Eckman et al., 2023. SimOpt: A Testbed for Simulation-Optimization Experiments
• Egele et al., 2023. Parallel multi-objective hyperparameter optimization with uniform normalization and bounded objectives
• Eggensperger et al., 2021. HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
• Ehrgott, 2005. Multicriteria Optimization
• Eichfelder, 2009. Scalarizations for adaptively solving multi-objective optimization problems
• Elsken et al., 2019. Neural architecture search: A survey
• Eriksson et al., 2019. Scalable global optimization via local bayesian optimization
• Eriksson et al., 2021. Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
• Farhan et al., 2020. Reinforcement Learning in AnyLogic Simulation Models: A Guiding Example using Pathmind
• Feldman et al., 2018. SCORE Allocations for Bi-objective Ranking and Selection
• Feliot et al., 2016. A Bayesian approach to constrained single- and multi-objective optimization
• Fiduccia et al., 1982. A linear-time heuristic for improving network partitions
• Fletcher, 1993. Resolving degeneracy in quadratic programming
• Fletcher, 2000. Stable reduced Hessian updates for indefinite quadratic programming
• Fortin et al., 2012. DEAP: Evolutionary Algorithms Made Easy
• Fourer et al., 2003. AMPL: A Modeling Language for Mathematical Programming
• Fowkes et al., 2022. PyCUTEst: an open source Python package of optimization test problems
• Fowkes et al., 2023. GALAHAD 4.0: an open source library of Fortran packages with C and Matlab interfaces for continuous optimization
• Frazier, 2018. A Tutorial on Bayesian Optimization
• Garnett, 2023. Bayesian Optimization
• Gavin, 2019. The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems
• Goh, 2017. Why Momentum Really Works
• Golovin et al., 2017. Google Vizier: A Service for Black-Box Optimization
• Gould et al., 2003. GALAHAD, a library of thread-safe Fortran 90 packages for large-scale nonlinear optimization
• Gould et al., 2015. CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization
• Graves, 2014. Generating Sequences With Recurrent Neural Networks
• Gray et al., 2019. OpenMDAO: An open-source framework for multidisciplinary design, analysis, and optimization
• Hadka, 2015. Platypus -- multiobjective optimization in Python
• Hanson et al., 1982. Algorithm 587: Two Algorithms for the Linearly Constrained Least Squares Problem
• Hart et al., 2017. Pyomo -- optimization modeling in Python
• H{"a}se et al., 2018. Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories
• Hayes et al., 2022. A practical guide to multi-objective reinforcement learning and planning
• He et al., 2009. Algorithm 897: VTDIRECT95: Serial and Parallel Codes for the Global Optimization Algorithm DIRECT
• He et al., 2009. Performance modeling and analysis of a massively parallel DIRECT -- part 1
• He et al., 2016. Deep Residual Learning for Image Recognition
• Hoffman et al., 2022. Optimizing molecules using efficient queries from property evaluations
• Huangfu et al., 2018. Parallelizing the dual revised simplex method
• Hunter et al., 2019. An introduction to multiobjective simulation optimization
• Ishibuchi et al., 2015. Modified distance calculation in generational distance and inverted generational distance
• Jain et al., 2013. An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: Handling constraints and extending to an adaptive approach
• Jones et al., 1993. Lipschitzian optimization without the Lipschitz constant
• Jones et al., 1998. Efficient global optimization of expensive black-box functions
• Kandasamy et al., 2020. Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
• Karl et al., 2023. Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview
• Karypis et al., 1998. A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
• Karypis et al., 1998. hMETIS: A hypergraph partitioning package
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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
• Akiba et al., 2019. Optuna: A next-generation hyperparameter optimization framework
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• Chang et al., 2023. ParMOO: A Python library for parallel multiobjective simulation optimization
• Chang et al., 2024. ParMOO: Python library for parallel multiobjective simulation optimization
• Chang et al., 2025. Designing a Framework for Solving Multiobjective Simulation Optimization Problems
• Chang et al., 2025. Repository for ``Designing a Framework for Solving Multiobjective Simulation Optimization Problems''
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• Chen et al., 2016. XGBoost: A Scalable Tree Boosting System
• Chen et al., 2023. An Integrated Multi-Physics Optimization Framework for Particle Accelerator Design
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• Fortin et al., 2012. DEAP: Evolutionary Algorithms Made Easy
• Forum, 2023. MPI: A Message-Passing Interface Standard
• Frostig et al., 2018. Compiling machine learning programs via high-level tracing
• Germann, 2021. Co-design in the Exascale Computing Project
• Golub et al., 2013. Matrix computations
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• Gray et al., 2019. OpenMDAO: An open-source framework for multidisciplinary design, analysis, and optimization
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• He et al., 2009. Algorithm 897: VTDIRECT95: Serial and Parallel Codes for the Global Optimization Algorithm DIRECT
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• Hert et al., 2020. dD Convex Hulls and Delaunay Triangulations
• Huang et al., 2019. Cpp-Taskflow: Fast Task-Based Parallel Programming Using Modern C++
• Huangfu et al., 2018. Parallelizing the dual revised simplex method
• Hudson et al., 2022. libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations
• Hudson et al., 2023. libEnsemble: A complete Python toolkit for dynamic ensembles of calculations
• Intel, 2025. oneAPI Threading Building Blocks (oneTBB)
• 2004. Information technology -- Programming languages -- Fortran -- Part 1: Base Language
• 2010. Information technology -- Programming languages -- Fortran -- Part 1: Base Language
• Kale et al., 1993. CHARM++: a portable concurrent object oriented system based on C++
• Kleen, 2005. A NUMA API for Linux
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• Kreps et al., 2011. Kafka: A distributed messaging system for log processing
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• Lasalle et al., 2013. Multi-threaded Graph Partitioning
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• Mayes, 2023. PySuperfish
• Menzel et al., 1987. Users guide for the POISSON/SUPERFISH group of codes
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• Neveu et al., 2023. Comparison of multiobjective optimization methods for the LCLS-II photoinjector
• NVIDIA Corporation, 2025. cuBLAS API
• NVIDIA HPC Compilers, 2025. CUDA Fortran programming guide
• OpenMP, 2015. OpenMP Application Programming Interface
• Paszke et al., 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Petitet et al., 2018. HPL -- A Portable Implementation of the High-Performance Linpack Benchmark for Distributed-Memory Computers
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• 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
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• Akiba et al., 2019. Optuna: A next-generation hyperparameter optimization framework
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• Balandat et al., 2020. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
• Balaprakash et al., 2018. DeepHyper: Asynchronous hyperparameter search for deep neural networks
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• Bansal et al., 2022. JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search
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• Barker et al., 2022. Introducing the FAIR Principles for research software
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• Betz et al., 1997. VPR: a new packing, placement and routing tool for FPGA research
• Biedron et al., 2019. FUN3D Manual: 13.6
• Biscani et al., 2020. A parallel global multiobjective framework for optimization: pagmo
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• Blank et al., 2020. pymoo: Multi-Objective Optimization in Python
• Blondel et al., 2022. Efficient and Modular Implicit Differentiation
• Bouhlel et al., 2019. A Python surrogate modeling framework with derivatives
• Bradbury et al., 2018. JAX: composable transformations of Python+NumPy programs
• Bratley et al., 1988. Algorithm 659: Implementing Sobol's Quasirandom Sequence Generator
• Brockman et al., 2016. OpenAI Gym
• Byrd et al., 2006. Knitro: An Integrated Package for Nonlinear Optimization
• Capps et al., 2016. IOzone Filesystem Benchmark
• Caron et al., 2024. qpbenchmark: Benchmark for quadratic programming solvers available in Python
• Chang et al., 2020. Algorithm 1012: DELAUNAYSPARSE: Interpolation via a Sparse Subset of the Delaunay Triangulation in Medium to High Dimensions
• Chang, 2020. Mathematical Software for Multiobjective Optimization Problems
• Chang et al., 2022. Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems
• Chang et al., 2023. ParMOO: A Python library for parallel multiobjective simulation optimization
• Chang et al., 2024. ParMOO: Python library for parallel multiobjective simulation optimization
• Chang et al., 2025. Designing a Framework for Solving Multiobjective Simulation Optimization Problems
• Chang et al., 2025. Repository for ``Designing a Framework for Solving Multiobjective Simulation Optimization Problems''
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• Gray et al., 2019. OpenMDAO: An open-source framework for multidisciplinary design, analysis, and optimization
• Hadka, 2015. Platypus -- multiobjective optimization in Python
• Hanson et al., 1982. Algorithm 587: Two Algorithms for the Linearly Constrained Least Squares Problem
• Harris et al., 2020. Array programming with NumPy
• Hart et al., 2017. Pyomo -- optimization modeling in Python
• H{"a}se et al., 2018. Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories
• He et al., 2009. Algorithm 897: VTDIRECT95: Serial and Parallel Codes for the Global Optimization Algorithm DIRECT
• He et al., 2016. Deep Residual Learning for Image Recognition
• Heek et al., 2024. Flax: A neural network library and ecosystem for JAX
• Heroux et al., 2020. Advancing Scientific Productivity through Better Scientific Software: Developer Productivity and Software Sustainability Report
• Hert et al., 2020. dD Convex Hulls and Delaunay Triangulations
• Holzmann, 2006. The power of 10: rules for developing safety-critical code
• Huang et al., 2019. Cpp-Taskflow: Fast Task-Based Parallel Programming Using Modern C++
• Huangfu et al., 2018. Parallelizing the dual revised simplex method
• Hudson et al., 2023. libEnsemble: A complete Python toolkit for dynamic ensembles of calculations
• Intel, 2025. oneAPI Threading Building Blocks (oneTBB)
• 2004. Information technology -- Programming languages -- Fortran -- Part 1: Base Language
• 2010. Information technology -- Programming languages -- Fortran -- Part 1: Base Language
• 2020. Information technology -- Programming languages -- C++
• Jain et al., 2022. tiktoken
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• Kale et al., 1993. CHARM++: a portable concurrent object oriented system based on C++
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• Karypis et al., 1998. hMETIS: A hypergraph partitioning package
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• Kolonay et al., 2011. Service oriented computing environment (SORCER) for large scale, distributed, dynamic fidelity aeroelastic analysis
• Kreps et al., 2011. Kafka: A distributed messaging system for log processing
• Le Digabel, 2011. Algorithm 909: NOMAD: Nonlinear Optimization with the MADS Algorithm
• Lee et al., 2015. pyDOE: The experimental design package for python
• Liang et al., 2024. AMF-Placer 2.0: Open-Source Timing-Driven Analytical Mixed-Size Placer for Large-Scale Heterogeneous FPGA
• Lindauer et al., 2022. SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
• Liuzzi et al., 2024. DFO-lib
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• Luu et al., 2011. VPR 5.0: FPGA CAD and architecture exploration tools with single-driver routing, heterogeneity and process scaling
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• Martins et al., 2009. pyMDO: An Object-Oriented Framework for Multidisciplinary Design Optimization
• Mayes, 2023. PySuperfish
• Menzel et al., 1987. Users guide for the POISSON/SUPERFISH group of codes
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• Mills et al., 2021. Toward performance-portable PETSc for GPU-based exascale systems
• Misitano et al., 2021. DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
• Moriwaki et al., 2018. Mordred: a molecular descriptor calculator
• Munson et al., 2015. TAO 3.5 Users Manual
• Murray et al., 2020. VTR 8: High-performance CAD and Customizable FPGA Architecture Modelling
• Narayanan et al., 2017. graph2vec: Learning Distributed Representations of Graphs
• NVIDIA Corporation, 2025. cuBLAS API
• NVIDIA HPC Compilers, 2025. CUDA Fortran programming guide
• O'Donoghue et al., 2016. Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding
• OpenMP, 2015. OpenMP Application Programming Interface
• Papazafeiropoulos, 2014. MATLAB Computational Geometry Toolbox version 1.2
• Paszke et al., 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Pedregosa et al., 2011. Scikit-learn: Machine learning in Python
• Pellegrini, 2009. Distillating knowledge about SCOTCH
• Petitet et al., 2018. HPL -- A Portable Implementation of the High-Performance Linpack Benchmark for Distributed-Memory Computers
• Pfisterer et al., 2022. YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization
• Raghunath et al., 2017. Global deterministic and stochastic optimization in a service oriented architecture
• Ragonneau et al., 2021. PDFO: Cross-Platform Interfaces for Powell’s Derivative-Free Optimization Solvers
• Rajarathnam et al., 2022. DREAMPlaceFPGA: An Open-Source Analytical Placer for Large Scale Heterogeneous FPGAs using Deep-Learning Toolkit
• Rose et al., 2012. The VTR project: architecture and CAD for FPGAs from verilog to routing
• Roy et al., 2023. Quasi-Monte Carlo Methods in Python
• Saves et al., 2024. SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes
• Shashaani et al., 2016. ASTRO-DF: Adaptive sampling trust-region optimization algorithms, heuristics, and numerical experience
• Shields et al., 2021. Bayesian reaction optimization as a tool for chemical synthesis
• Slepicka, 2020. Poisson Superfish via Docker
• Stall et al., 2019. Make scientific data FAIR
• Stellato et al., 2020. OSQP: an operator splitting solver for quadratic programs
• Tavares et al., 2022. Parallel Strategies for Direct Multisearch
• Thacker et al., 2010. Algorithm 905: SHEPPACK: Modified Shepard algorithm for interpolation of scattered multivariate data
• Tian et al., 2017. PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]
• Trott et al., 2022. Kokkos 3: Programming Model Extensions for the Exascale Era
• Vanaret et al., 2024. Unifying nonlinearly constrained nonconvex optimization
• van der Maaten et al., 2008. Visualizing Data using t-SNE
• Vandevender et al., 1982. The SLATEC Mathematical Subroutine Library
• Virtanen et al., 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
• Voss et al., 2019. TBB on NUMA Architectures
• VTR Developers, 2024. Verilog-to-routing documentation
• Wang et al., 2022. Pyomo.DOE: An open-source package for model-based design of experiments in Python
• Watson et al., 1997. Algorithm 777: HOMPACK90: A suite of Fortran 90 codes for globally convergent homotopy algorithms
• Whaley et al., 2001. Automated empirical optimizations of software and the ATLAS project
• Wu et al., 2025. ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
• Wächter et al., 2006. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
• Xiong et al., 2023. DREAMPlaceFPGA-MP: An Open-Source GPU-Accelerated Macro Placer for Modern FPGAs with Cascade Shapes and Region Constraints
• Yun et al., 2019. Graph transformer networks
• Zhang, 2023. PRIMA: Reference Implementation for Powell's Methods with Modernization and Amelioration
• Zhu et al., 1997. Algorithm 778: L-BFGS-B: Fortran Subroutines for Large-Scale Bound-Constrained Optimization

computational geometry

• Aurenhammer et al., 2013. Voronoi diagrams and Delaunay triangulations
• Bambade et al., 2022. PROX-QP: Yet another Quadratic Programming Solver for Robotics and beyond
• Barber et al., 1996. The Quickhull algorithm for convex hulls
• Boissonnat et al., 2009. Incremental construction of the Delaunay triangulation and the Delaunay graph in medium dimension
• Bowyer, 1981. Computing Dirichlet tessellations
• Boyd et al., 2004. Convex optimization
• Chang et al., 2018. Computing the umbrella neighbourhood of a vertex in the Delaunay triangulation and a single Voronoi cell in arbitrary dimension
• Chang et al., 2018. A polynomial time algorithm for multivariate interpolation in arbitrary dimension via the Delaunay triangulation
• Chang et al., 2018. Predicting system performance by interpolation using a high-dimensional Delaunay triangulation
• Chang et al., 2020. Algorithm 1012: DELAUNAYSPARSE: Interpolation via a Sparse Subset of the Delaunay Triangulation in Medium to High Dimensions
• Chang, 2020. Mathematical Software for Multiobjective Optimization Problems
• Chang et al., 2024. Remark on Algorithm 1012: Computing projections with large data sets
• Chen et al., 2004. Optimal Delaunay triangulations
• Cheng et al., 2012. Delaunay Mesh Generation
• Choudhary et al., 2019. Polynomial-sized topological approximations using the permutahedron
• Cignoni et al., 1998. DeWall: A fast divide and conquer Delaunay triangulation algorithm in $\mathbbE^d$
• Dantzig, 1998. Linear Programming and Extensions
• de Berg et al., 2008. Computational Geometry: Algorithms and Applications
• Delaunay, 1934. Sur la sph'ere vide
• Devillers et al., 2001. Walking in a triangulation
• Diamond et al., 2016. CVXPY: A Python-embedded modeling language for convex optimization
• Domahidi et al., 2013. ECOS: An SOCP solver for embedded systems
• Edelsbrunner, 1989. An acyclicity theorem for cell complexes in d dimensions
• Edelsbrunner et al., 1990. Simulation of simplicity: a technique to cope with degenerate cases in geometric algorithms
• Fortune, 1987. A sweepline algorithm for Voronoi diagrams
• Fukuda, 2004. Is it possible to compute only the adjacencies of Voronoi cells in the Voronoi diagram efficiently?
• Gillette et al., 2022. Data-driven geometric scale detection via Delaunay interpolation
• Gillette et al., 2024. Algorithm 1049: The Delaunay Density Diagnostic
• Gorban et al., 2017. Stochastic separation theorems
• Hanson et al., 1982. Algorithm 587: Two Algorithms for the Linearly Constrained Least Squares Problem
• Hert et al., 2020. dD Convex Hulls and Delaunay Triangulations
• Huangfu et al., 2018. Parallelizing the dual revised simplex method
• Johnson et al., 1990. Minimax and maximin distance designs
• Klee et al., 1972. How good is the simplex algorithm?
• Klee, 1980. On the complexity of d-dimensional Voronoi diagrams
• Liu et al., 2019. Nonparametric functional approximation with Delaunay triangulation learner
• Lux et al., 2018. Novel meshes for multivariate interpolation and approximation
• Lux et al., 2018. Predictive modeling of I/O characteristics in high performance computing systems
• Lux et al., 2021. Interpolation of sparse high-dimensional data
• Megiddo, 1991. On finding primal- and dual-optimal bases
• Miller et al., 2008. Linear-size meshes
• M"obius, 1827. Der barycentrische Calcul
• M{"u}cke et al., 1999. Fast randomized point location without preprocessing in two- and three-dimensional Delaunay triangulations
• Omohundro, 1990. Geometric learning algorithms
• Papazafeiropoulos, 2014. MATLAB Computational Geometry Toolbox version 1.2
• Polianskii et al., 2020. Voronoi Graph Traversal in High Dimensions with Applications to Topological Data Analysis and Piecewise Linear Interpolation
• Rajan, 1994. Optimality of the Delaunay triangulation in $\R^d$
• Shewchuk, 2002. What is a good linear finite element? Interpolation, conditioning, anisotropy, and quality measures
• Smale, 1998. Mathematical problems for the next century
• Stellato et al., 2020. OSQP: an operator splitting solver for quadratic programs
• Su et al., 1997. A comparison of sequential Delaunay triangulation algorithms
• Virtanen et al., 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
• Watson, 1981. Computing the n-dimensional Delaunay tessellation with application to Voronoi polytopes

design of experiments

• 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
• Amos et al., 2020. Algorithm 1007: QNSTOP: Quasi-Newton algorithm for stochastic optimization
• Bratley et al., 1988. Algorithm 659: Implementing Sobol's Quasirandom Sequence Generator
• Chang, 2020. Mathematical Software for Multiobjective Optimization Problems
• Chang et al., 2022. Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems
• Chang et al., 2023. ParMOO: A Python library for parallel multiobjective simulation optimization
• Chang et al., 2024. ParMOO: Python library for parallel multiobjective simulation optimization
• Chang et al., 2025. Designing a Framework for Solving Multiobjective Simulation Optimization Problems
• Chang et al., 2025. Repository for ``Designing a Framework for Solving Multiobjective Simulation Optimization Problems''
• Chen et al., 2023. An Integrated Multi-Physics Optimization Framework for Particle Accelerator Design
• Chowdhary et al., 2024. PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments
• Dalal et al., 2008. Low discrepancy sequences for Monte Carlo simulations on reconfigurable platforms
• Garg et al., 2023. SF-SFD: Stochastic optimization of Fourier coefficients for space-filling designs
• Gorban et al., 2017. Stochastic separation theorems
• Hickernell, 1998. A generalized discrepancy and quadrature error bound
• Joe et al., 2003. Remark on Algorithm 659: Implementing Sobol's Quasirandom Sequence Generator
• Joe et al., 2008. Constructing Sobol sequences with better two-dimensional projections
• Johnson et al., 1990. Minimax and maximin distance designs
• Kuipers et al., 1974. Uniform distribution of sequences
• Lax, 2002. Functional analysis
• Lee et al., 2015. pyDOE: The experimental design package for python
• Liu et al., 2017. An adaptive sampling approach for Kriging metamodeling by maximizing expected prediction error
• Lux et al., 2018. Novel meshes for multivariate interpolation and approximation
• M{"u}ller, 2017. SOCEMO: Surrogate optimization of computationally expensive multiobjective problems
• Myers et al., 2016. Response Surface Methodology: Process and Design Optimization Using Designed Experiments
• Niederreiter, 1988. Low-discrepancy and low-dispersion sequences
• Pickering et al., 2022. Discovering and forecasting extreme events via active learning in neural operators
• Pronzato, 2017. Minimax and maximin space-filling designs: some properties and methods for construction
• Roy et al., 2023. Quasi-Monte Carlo Methods in Python
• Sapsis et al., 2022. Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modelling with Gaussian process regression
• Saves et al., 2024. SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes
• Sobieszczanski-Sobieski et al., 2015. Multidisciplinary Design Optimization Supported by Knowledge Based Engineering
• Sobol, 1967. Distribution of points in a cube and approximate evaluation of integrals
• Tao, 2011. An Epsilon of Room: Pages from year three of a mathematical blog
• Viana, 2016. A tutorial on Latin hypercube design of experiments
• Virtanen et al., 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
• Wang et al., 2022. Pyomo.DOE: An open-source package for model-based design of experiments in Python
• Wang et al., 2023. Design strategies and approximation methods for high-performance computing variability management
• Wong et al., 1997. Sampling with Hammersley and Halton points

quantum computing

• Aharonov et al., 2008. Adiabatic quantum computation is equivalent to standard quantum computation
• Albash et al., 2018. Adiabatic quantum computation
• Alghassi et al., 2019. Graver Bases via Quantum Annealing with Application to Non-Linear Integer Programs
• Andriyash et al., 2016. Boosting integer factoring performance via quantum annealing offsets
• Barahona, 1982. On the computational complexity of Ising spin glass models
• Biamonte et al., 2017. Quantum machine learning
• Boixo et al., 2014. Evidence for quantum annealing with more than one hundred qubits
• Boothby et al., 2018. Next-generation topology of D-Wave quantum processors
• Borle et al., 2019. Analyzing the quantum annealing approach for solving linear least squares problems
• Cai et al., 2014. A practical heuristic for finding graph minors
• Chang et al., 2019. Experimental realization of quantum algorithms for a linear system inspired by adiabatic quantum computing
• Das et al., 2005. Quantum annealing and related optimization methods
• Dattani, 2019. Quadratization in discrete optimization and quantum mechanics
• Dridi et al., 2017. Prime factorization using quantum annealing and computational algebraic geometry
• Elgart et al., 2012. A note on the switching adiabatic theorem
• Jiang et al., 2018. Quantum annealing for prime factorization
• Kadowaki et al., 1998. Quantum annealing in the transverse Ising model
• Karimi et al., 2019. Practical integer-to-binary mapping for quantum annealers
• Khoshaman et al., 2018. Quantum variational autoencoder
• Pakin, 2018. Performing fully parallel constraint logic programming on a quantum annealer
• Peng et al., 2008. Quantum adiabatic algorithm for factorization and its experimental implementation
• Ray et al., 1989. Sherrington-Kirkpatrick model in a transverse field: Absence of replica symmetry breaking due to quantum fluctuations
• Rosenberg, 1975. Reduction of bivalent maximization to the quadratic case
• Santoro et al., 2002. Theory of quantum annealing of an Ising spin glass
• Shor, 1999. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
• Steane, 1998. Quantum computing