Mixed Integer Optimization

David Smith discusses the ompr package in R:

Counterintuitively, numerical optimizations are easiest (though rarely actually easy) when all of the variables are continuous and can take any value. When integer variables enter the mix, optimization becomes much, much harder. This typically happens when the optimization is constrained by a limited selection of objects, for example packages in a weight-limited cargo shipment, or stocks in a portfolio constrained by sector weightings and transaction costs. For tasks like these, you often need an algorithm for a specialized type of optimization: Mixed Integer Programming.

For problems like these, Dirk Schumacher has created the ompr package for R. This package provides a convenient syntax for describing the variables and contraints in an optimization problem. For example, take the classic “knapsack” problem of maximizing the total value of objects in a container subject to its maximum weight limit.

Read the whole thing.

Related Posts

R Data Frames And stringsAsFactors

John Mount recommends setting stringsAsFactors = FALSE for data frames in R: R often uses a concept of factors to re-encode strings. This can be too early and too aggressive. Sometimes a string is just a string. Tibbles have this set by default.  For an explanation as to why it defaults to TRUE for data frames, Roger […]

Read More

The Microsoft Team Data Science Process Lifecycle Versus CRISP-DM

Melody Zacharias compares Microsoft’s Team Data Science Process lifecycle with the CRISP-DM process: As I pointed out in my previous blog, the TDSP lifecycle is made up of five iterative stages: Business Understanding Data Acquisition and Understanding Modeling Deployment Customer Acceptance This is not very different from the six major phases used by the Cross […]

Read More


December 2016
« Nov Jan »