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.

中文翻译：

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快速矩阵乘法的稳定性改进

我们实现了增强拉格朗日方法来最小化约束最小二乘成本函数，该函数旨在找到具有矩阵乘法张量有界最大值元素的稀疏多元分解。我们使用这种方法来获得新的分解和分解的参数族。使用这些参数化，可以发现更快、更稳定的矩阵乘法算法。