We provide a new numerical procedure for constructing low coherence matrices, Trust-Region Stochastic Tuning for Matrix Incoherence (TRSTMI) and detail the results of experiments with a CPU/GPU parallelized implementation of this method. These trials suggest the superiority of this approach over other existing methods when the size of the matrix is large. We also present new conjectures on optimal complex matrices motivated and guided by the experimental results.

翻译：我们提供了一个新的数字程序,用于构建低一致性矩阵,即用于矩阵不一致性的信托-区域斯托卡图(TRSTMI),并详细介绍了以CPU/GPU平行实施这种方法的实验结果。这些试验表明,当矩阵大小较大时,这一方法优于其他现有方法。我们还提出了关于以实验结果为动力并受其指导的最佳复杂矩阵的新假设。