This work presents a novel approach to compute the eigenvalues of non-Hermitian matrices using an enhanced shifted QR algorithm. The existing QR algorithms fail to converge early in the case of non-hermitian matrices, and our approach shows significant improvement in convergence rate while maintaining accuracy for all test cases. In this work, though our prior focus will be to address the results for a class mid- large sized non-Hermitian matrices, our algorithm has also produced significant improvements in the case of comparatively larger matrices such as 50 x 50 non-Hermitian matrices

翻译：本文提出了一种利用增强型位移QR算法计算非厄米矩阵特征值的新方法。现有QR算法在处理非厄米矩阵时存在早期收敛困难的问题，而本方法在保持所有测试案例精度的同时，显著提升了收敛速度。本研究虽然主要针对中等至大规模非厄米矩阵的结果进行分析，但该算法在更大规模矩阵（例如50×50非厄米矩阵）的计算中也取得了显著改进。