To do these pivots efficiently, both codes use the same sparse linear algebra package. PATH has a crashing scheme that allows it to quickly improve the user given starting point before starting to solve the linear subproblems. This frequently speeds up solution time. In many cases, this enables the linear subproblems to be solved, leading to a model solution. This typically helps modelers at model development time.
PATH has many more solution options to enable it solve difficult models.
The code automatically tries useful options on difficult problems using a restart procedure. PATH implements a nonmonotone watchdog technique. Stalling is frequently circumvented by allowing larger steps to be taken toward solutions. PATH has many more diagnostic features that help uncover problems in a model. In particular, singularities in the model, zero rows and columns and several measures of optimality are returned to the user.