Cassowary is an incremental constraint solving toolkit that efficiently solves systems of linear equalities and inequalities. Constraints may be either requirements or preferences. Client code specifies the constraints to be maintained, and the solver updates the constrained variables to have values that satisfy the constraints.
Cassowary was developed by Greg J. Badros, Alan Borning and Peter J. Stuckey, and was optimized for user interface applications.[1] Badros used Cassowary amongst others for implementing Constraint Cascading Style Sheets (CCSS), an extension to Cascading Style Sheets (CSS). CCSS adds support for layout constraints. These allow designers to describe the layout of a web page in a more flexible manner. Cassowary is used to solve these constraints and calculate the final layout.
^Kiwi, a replacement for Casuarius/Cassowary. The new solver removes that bottleneck. It still uses the same Cassowary algorithm, but it's a from-scratch implementation of the algorithm based on the Cassowary paper, not the existing Cassowary source code. While the Cassowary algorithm is good, the existing C++ implementation has many inefficiencies. The new solver is anywhere between 12x and 500x faster depending on the problem (40x typical) and uses around 5x less memory. And as I typically do, I chose to write the Python bindings by hand in C++ rather than use Cython. The resulting code is faster, and we now have one less external dependency.