cvxoptdes: convex optimal experiment design in R

Abstract

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.

arXiv comment43 pages, 11 figures