我想把一个算法从MATLAB移植到Python。所述算法的一个步骤涉及到取A^(-1/2)，其中A是9x9平方复矩阵。据我所知，矩阵的平方根(及其逆矩阵的推广)不是唯一的。在

我一直在试验scipy.linalg.fractional_matrix_power和使用A^(-1/2) = exp((-1/2)*log(A))和numpy内置的expm和{}函数的近似值。前者非常差，只提供了3个小数位的精度，而后者对于左上角的元素来说是非常正确的，但是当您向下或向右移动时，它会变得越来越差。这可能是，也可能不是表达式的完全有效的数学解，但它不足以满足此应用。在

因此，我希望在Python中直接实现MATLAB的矩阵幂算法，以便每次都能100%地确认相同的结果。有没有人有任何关于这将如何工作的见解或文档？这个算法的并行性越强越好，因为最终的目标是在OpenCL中重写它以实现GPU加速。在

编辑：MCVE按要求：[[(0.591557294607941+4.33680868994202e-19j), (-0.219707725574605-0.35810724986609j), (-0.121305654177909+0.244558388829046j), (0.155552026648172-0.0180264818714123j), (-0.0537690384136066-0.0630740244116577j), (-0.0107526931263697+0.0397896274845627j), (0.0182892503609312-0.00653264433724856j), (-0.00710188853532244-0.0050445035279044j), (-2.20414002823034e-05+0.00373184532662288j)], [(-0.219707725574605+0.35810724986609j), (0.312038814492119+2.16840434497101e-19j), (-0.109433401402399-0.174379997015402j), (-0.0503362231078033+0.108510948023091j), (0.0631826956936223-0.00992931123813742j), (-0.0219902325360141-0.0233215237172002j), (-0.00314837555001163+0.0148621558916679j), (0.00630295247506065-0.00266790359447072j), (-0.00249343102520442-0.00156160619280611j)], [(-0.121305654177909-0.244558388829046j), (-0.109433401402399+0.174379997015402j), (0.136649392858215-1.76182853028894e-19j), (-0.0434623984527311-0.0669251299161109j), (-0.0168737559719828+0.0393768358149159j), (0.0211288536117387-0.00417146769324491j), (-0.00734306979471257-0.00712443264825166j), (-0.000742681625102133+0.00455752452374196j), (0.00179068247786595-0.000862706240042082j)], [(0.155552026648172+0.0180264818714123j), (-0.0503362231078033-0.108510948023091j), (-0.0434623984527311+0.0669251299161109j), (0.0467980890488569+5.14996031930615e-19j), (-0.0140208255975664-0.0209483313237692j), (-0.00472995448413803+0.0117916398375124j), (0.00589653974090387-0.00134198920550751j), (-0.00202109265416585-0.00184021636458858j), (-0.000150793859056431+0.00116822322464066j)], [(-0.0537690384136066+0.0630740244116577j), (0.0631826956936223+0.00992931123813742j), (-0.0168737559719828-0.0393768358149159j), (-0.0140208255975664+0.0209483313237692j), (0.0136137125669776-2.03287907341032e-20j), (-0.00387854073283377-0.0056769786724813j), (-0.0011741038702424+0.00306007798625676j), (0.00144000687517355-0.000355251914809693j), (-0.000481433965262789-0.00042129815655098j)], [(-0.0107526931263697-0.0397896274845627j), (-0.0219902325360141+0.0233215237172002j), (0.0211288536117387+0.00417146769324491j), (-0.00472995448413803-0.0117916398375124j), (-0.00387854073283377+0.0056769786724813j), (0.00347771689075251+8.21621958836671e-20j), (-0.000944046302699304-0.00136521328407881j), (-0.00026318475762475+0.000704212317211994j), (0.00031422288569727-8.10033316327328e-05j)], [(0.0182892503609312+0.00653264433724856j), (-0.00314837555001163-0.0148621558916679j), (-0.00734306979471257+0.00712443264825166j), (0.00589653974090387+0.00134198920550751j), (-0.0011741038702424-0.00306007798625676j), (-0.000944046302699304+0.00136521328407881j), (0.000792908166233942-7.41153828847513e-21j), (-0.00020531962049495-0.000294952695922854j), (-5.36226164765808e-05+0.000145645628243286j)], [(-0.00710188853532244+0.00504450352790439j), (0.00630295247506065+0.00266790359447072j), (-0.000742681625102133-0.00455752452374196j), (-0.00202109265416585+0.00184021636458858j), (0.00144000687517355+0.000355251914809693j), (-0.00026318475762475-0.000704212317211994j), (-0.00020531962049495+0.000294952695922854j), (0.000162971629601464-5.39321759384574e-22j), (-4.03304806590714e-05-5.77159110863666e-05j)], [(-2.20414002823034e-05-0.00373184532662288j), (-0.00249343102520442+0.00156160619280611j), (0.00179068247786595+0.000862706240042082j), (-0.000150793859056431-0.00116822322464066j), (-0.000481433965262789+0.00042129815655098j), (0.00031422288569727+8.10033316327328e-05j), (-5.36226164765808e-05-0.000145645628243286j), (-4.03304806590714e-05+5.77159110863666e-05j), (3.04302590501313e-05-4.10281583826302e-22j)]]