  Affine variational inequalities (AVI) are an important problem class that
generalize systems of linear equations, linear complementarity problems and
optimality conditions for quadratic programs. This paper describes PATHAVI, a
structure-preserving pivotal approach, that can process (solve or determine
infeasible) large-scale sparse instances of the problem efficiently, with
theoretical guarantees and at high accuracy. PATHAVI implements a strategy that
is known to process models with good theoretical properties without reducing
the problem to specialized forms, since such reductions may destroy structure
in the models and can lead to very long computational times. We demonstrate
formally that PATHAVI implicitly follows the theoretically sound iteration
paths, and can be implemented in a large scale setting using existing sparse
linear algebra and linear programming techniques without employing a reduction.
We also extend the class of problems that PATHAVI can process. The paper
demonstrates the effectiveness of our approach by comparison to the PATH solver
used on a complementarity reformulation of the AVI in the context of
applications in friction contact and Nash Equilibria problems. PATHAVI is a
general purpose solver, and freely available under the same conditions as PATH .
