  Linear constrained optimization techniques have been applied to many
real-world settings. In recent years, inferring the unknown parameters and
functions inside an optimization model has also gained traction. This inference
is often based on existing observations and/or known parameters. Consequently,
such models require reliable, easily accessed, and easily interpreted examples
to be evaluated. To facilitate research in such directions, we provide a
modified dataset based on dietary behaviors of different groups of people,
their demographics, and pre-existing conditions, among other factors. This data
is gathered from the National Health and Nutrition Examination Survey (NHANES)
and complemented with the nutritional data from the United States Department of
Agriculture (USDA). We additionally provide tailored datasets for hypertension
and pre-diabetic patients as groups of interest who may benefit from targetted
diets such as the Dietary Approaches to Stop Hypertension (DASH) eating plan.
The data is compiled and curated in such a way that it is suitable as input to
linear optimization models. We hope that this data and its supplementary,
open-accessed materials can accelerate and simplify interpretations and
research on linear optimization and constrained inference models. The complete
dataset can be found in the following repository:
https://github.com/CSSEHealthcare/InverseLearning
