Mathematical optimization problems arise in fascinating ways throughout food and nutrition policy: in regression, the fundamental algorithms are based on least-squares and maximum likelihood optimization; in economics, demand theory centers on utility maximization subject to a budget constraint, and supply theory uses cost minimization subject to a production target; in nutrition policy, USDA’s Thrifty Food Plan minimizes a squared distance function subject to both a budget constraint and nutrition constraints; and in logistics, maximizing profit while minimizing the delivery cost from distribution centers to grocery stores explains key developments in the food retail industry. This course teaches (or, for some students, re-teaches) selected topics in differential calculus and matrix algebra as preparation for constrained optimization using the Lagrangian method and computationally intensive approaches. The course is designed for PhD students (commonly in their first year) and mathematically curious master’s students (commonly in their second year) as preparation for further study of advanced quantitative research tools.
Mathematical Optimization Methods for Food and Nutrition Policy Research
|Semester/Term||SIS Number||Meeting Time||Location||Instructors|
|Fall 2018||83786||N/A||Parke Wilde|