Stability improvements for fast matrix multiplication

We implement an Augmented Lagrangian method to minimize a constrained least-squares cost function designed to find sparse polyadic decompositions with elements of bounded maximal value of matrix multiplication tensors. We use this method to obtain new decompositions and parameter families of decompositions. Using these parametrizations, faster and more stable matrix multiplication algorithms are discovered.