ConstraintTrees.jl

Package ConstraintTrees.jl provides a simple data structure ConstraintTree for organizing the contents of linear and quadratic constrained optimization problems. As a main goal, it abstracts over the distinction between constraints and variables, allowing much tidier representation for many kinds of complex constraint systems.

The primary purpose of ConstraintTrees.jl is to work with COBREXA.jl; but the package is otherwise completely independent, lightweight, dependency-free and usecase-agnostic. Generally, it is intended to be used with JuMP and the documentation uses JuMP for demonstrations, but any other solver framework will do just as well.

The package is structured as follows:

• There is no representation for variables in the model; instead, values depend on anonymous numbered variables, and, if suitable, special named values may "implicitly" serve as representations for variables. This assumption erases the distinction between a "simple" variable and a complex derived linear combination, allowing more freedom in model construction.
• Variables may be combined into LinearValues and QuadraticValues, which are affine combinations and quadratic-affine combinations (respecitively) of values of some selected variables.
• Values may be bounded to an interval or exact value using a Constraint
• A collection of named Constraints is called a ConstraintTree; it behaves mostly as a specialized Symbol-keyed dictionary.
• ConstraintTrees can be very easily organized into subdirectories, combined and made independent on each other using operators ^, *, and + – this forms the basis of the "tidy" algebra of constraints.
• A variable assignment, which is typically the "solution" for a given constraint tree, can be combined with a ConstraintTree to create a "value tree" via substitute_values, which enables browsing of the optimization results in the very same structure as the input ConstraintTree.

You can follow the examples in documentation and the docstrings of package contents for more details.