  This paper presents PIQP, a high-performance toolkit for solving generic
sparse quadratic programs (QP). Combining an infeasible Interior Point Method
(IPM) with the Proximal Method of Multipliers (PMM), the algorithm can handle
ill-conditioned convex QP problems without the need for linear independence of
the constraints. The open-source implementation is written in C++ with
interfaces to C, Python, Matlab, and R leveraging the Eigen3 library. The
method uses a pivoting-free factorization routine and allocation-free updates
of the problem data, making the solver suitable for embedded applications. The
solver is evaluated on the Maros-M\'esz\'aros problem set and optimal control
problems, demonstrating state-of-the-art performance for both small and
large-scale problems, outperforming commercial and open-source solvers.
