cvxoptdes: convex optimal experiment design in R

@misc{chau2026cvxoptdes,
  title = {{cvxoptdes: convex optimal experiment design in R}},
  author = {Joris Chau and Alexandre Segers and Martin Otava and Stephen Karanja},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.14288},
  eprint = {2610.14288},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.14288},
}

TY  - UNPB
TI  - cvxoptdes: convex optimal experiment design in R
AU  - Joris Chau
AU  - Alexandre Segers
AU  - Martin Otava
AU  - Stephen Karanja
PY  - 2026
DA  - 2026/10/09
PB  - arXiv
DO  - 10.48550/arxiv.2610.14288
UR  - https://arxiv.org/abs/2610.14288
AB  - The cvxoptdes R-package provides a unified framework to perform convex optimal experiment design and online response optimization for linear, generalized linear and additive, nonlinear, and random intercept mixed models, using exclusively open-source solvers. It supports optimization and augmentation of weighted, penalized and convex constrained continuous designs across a discrete grid of candidate points and rounding to integer-valued designs using point-exchange algorithms or mixed-integer programming for orthogonally constrained designs, including the class of orthogonal minimally aliased response surface (OMARS) designs. Models and design constraints are defined and updated in an object-oriented context with the aim to facilitate the process of sequential (penalized) design augmentation analogous to online design procedures known from active learning and Bayesian optimization.
ER  -