Optimal Generalized Decision Trees via Integer Programming. - Matt Menickelly; Oktay Günlük; Jayant Kalagnanam; Katya Scheinberg (CoRR, 2016)

Manifold Sampling for ℓ1 Nonconvex Optimization. - Jeffrey Larson 001; Matt Menickelly; Stefan M. Wild (SIAM J. Optim., 2016)

A Novel l0-Constrained Gaussian Graphical Model for Anomaly Localization. - Dzung T. Phan; Tsuyoshi Idé; Jayant Kalagnanam; Matt Menickelly; Katya Scheinberg (ICDM Workshops, 2017)

Stochastic optimization using a trust-region method and random models. - Ruobing Chen 001; Matt Menickelly; Katya Scheinberg (Math. Program., 2018)

Derivative-free optimization methods. - Jeffrey Larson 001; Matt Menickelly; Stefan M. Wild (Acta Numer., 2019)

Convergence Rate Analysis of a Stochastic Trust-Region Method via Supermartingales. - Jose H. Blanchet; Coralia Cartis; Matt Menickelly; Katya Scheinberg (INFORMS J. Optim., 2019)

Tuning Multigrid Methods with Robust Optimization. - Jed Brown; Yunhui He; Scott P. MacLachlan; Matt Menickelly; Stefan M. Wild (CoRR, 2020)

A survey of nonlinear robust optimization. - Sven Leyffer; Matt Menickelly; Todd S. Munson; Charlie Vanaret; Stefan M. Wild (INFOR Inf. Syst. Oper. Res., 2020)

Derivative-free robust optimization by outer approximations. - Matt Menickelly; Stefan M. Wild (Math. Program., 2020)

On the Solution of ℓ0-Constrained Sparse Inverse Covariance Estimation Problems. - Dzung T. Phan; Matt Menickelly (INFORMS J. Comput., 2021)

Optimal decision trees for categorical data via integer programming. - Oktay Günlük; Jayant Kalagnanam; Minhan Li; Matt Menickelly; Katya Scheinberg (J. Glob. Optim., 2021)

Manifold Sampling for Optimizing Nonsmooth Nonconvex Compositions. - Jeffrey Larson 001; Matt Menickelly; Baoyu Zhou (SIAM J. Optim., 2021)

Tuning Multigrid Methods with Robust Optimization and Local Fourier Analysis. - Jed Brown; Yunhui He; Scott P. MacLachlan; Matt Menickelly; Stefan M. Wild (SIAM J. Sci. Comput., 2021)

TROPHY: Trust Region Optimization Using a Precision Hierarchy. - Richard J. Clancy; Matt Menickelly; Jan Hückelheim; Paul D. Hovland; Prani Nalluri; Rebecca Gjini (ICCS (1), 2022)

Improving PyDDA's atmospheric wind retrievals using automatic differentiation and Augmented Lagrangian methods. - Robert Jackson; Rebecca Gjini; Sri Hari Krishna Narayanan; Matt Menickelly; Paul D. Hovland; Jan Hückelheim; Scott M. Collis (SciPy, 2022)

Latency considerations for stochastic optimizers in variational quantum algorithms. - Matt Menickelly; Yunsoo Ha; Matthew Otten (Quantum, 2023)

Structure-aware methods for expensive derivative-free nonsmooth composite optimization. - Jeffrey Larson 001; Matt Menickelly (Math. Program. Comput., 2024)

Stochastic average model methods. - Matt Menickelly; Stefan M. Wild (Comput. Optim. Appl., 2024)

Convergence-Guaranteed Elastic Net Graphical Model Estimation with Applications to Anomaly Localization. - Dzung T. Phan; Matt Menickelly; Tsuyoshi Idé; Jayant Kalagnanam (SDM, 2025)

Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms. - Yunsoo Ha; Sara Shashaani; Matt Menickelly (INFORMS J. Comput., 2025)

A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms. - Jeffrey Larson 001; Matt Menickelly; Jiahao Shi (INFORMS J. Comput., 2025)