gong 发表于 2022-12-10 15:51:09

利用arpack求解实对称矩阵的特征值和特征向量

本帖最后由 gong 于 2022-12-10 16:32 编辑

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ENAAADXn34wvP+09u3b5/evXv3fHSs9+/fP+/90Js3b7rtqdUfckzZP9WWoq9uAwCAOXcppFvFa9lW988dh6n40MqR6rEAADDnEl/tiEK3LHz3EDkV0AAAjDpNIX1kUdv6hBoAALa4SyG9xyfMEV9uZaG8R34AAJhyt0+ks9gdLXgjPovnsogGAIBbuOtXO7IYPvLT4/rTagAA2MMpviO9pZg+uhAHAICWuxTStyh8FdgAABzpFJ9Iz5n7esZU0TwVBwAAo+72D7LUsuCd6gtlf6u9busV0nPzAADAlLv9E+EAAHBluxfSrU96Sz71BQDgEfhEGgAABlzilw0BAOBsFNIAADBAIQ0AAAMU0gAAMEAhDQAAAw75qx29P4GXf/ru7P0AADDHn78DAIABvtoBAAADXn33u9/96hPpX/3VX336nd/5neejcWf/6sZcPwAAzPHVDgAAGOCrHQAAMEAhDQAAAxTSAAAwQCENAAADFNIAADBAIQ0AAAMU0gAAMEAhDQAAAxTSAAAwQCENAAADFNIAADBAIQ0AAAMU0gAAMEAhDQAAAxTSAAAwQCENAAADFNIAADBAIQ0AAAMU0gAAMODVL//yL3943n/6wz/8w6c//uM/fj4CAAB6Xv3Jn/zJV4X0r/zKrzz91m/91vMRAADQ8/onf/Inn3L7sR/7sedmAABgiu9IAwDAAIU0AAAMOKyQfv/+/UdbqdXG4zj6+XX98AimrmHXOMD5/bCQ/u6v7famHXnevHnzte2q9r6Z7Z0PuJ6594Hoy/dO7xcA5/W1T6R/7B/9F897x7tCgb31ZtaKOftjLm25gR/9/B6dH440df3m+06K/S2vRQCO87VC+m//r3/xvLe/qxc9V18/AAD7evXhC1/ufffXnt7893/69N3/7X/+8nCr+lOVUvnpSmtM79OXcmw9Zm2hOxXfmn9N/rn46I/jclydf2p9U1pzp3oNaW7ukGPquFaeqdyhl6M1tmUufyjHpKX54VbiOq2vy6VtANzfqw//5n/58E9+/tee/vSLg7/+679++rM/+7Mf9Owgi5mpYqfVV7fPHYderpal8WtytvTioz2UfeXYrWvp5cr9Olcvd689RF+oc5fm4qdil+jFrV0L3MvSa9X1C3BOr5/+4//66X/45//86X/67/6zp//kP9/3jTre+GOLm8Aa5Q3jUW8g93xM9fkdVeZZ+3hifMx9q+f3FnMAAC/L174j/f/8q/GiakoWTWtNFVlZhOX2aI5+fJn3EQvMvN7KDQBgb18rpPf6RPoWhUsUS/X2SI58fPH87J3zbMrzFptiGgDY25eF9L/5p//k6R//j3962CfSI7LYS3OF0NkLpa3rO+rxLc279/yR76gi96hzBbdQvybytQLA+fzwr3Z84e3bt0/v3r17PhrXKmTmiuK5/jA1Zu2NZip+bn1rZK6ML3MvbQtL569zlfMv2a/VfXlcquPmxrTmDq35W5bmLy3NDbew5BotXycAnNOrD//yVz/8l7/4v355sPdf7QAAgEd1yCfSAADw6L72y4YAAMAyCmkAABigkAYAgAG7f0e69dvoyW+fn1/v+XvEvygQj8E1+Zj8xQse1dL3aOA2Xv/FX/zFU27f//73n5u3iRd0vqjL/RBvAr03giu42vrXrrd+3nK78nNWu9pz+NJ5vuDr6vfn2ID7eP0TP/ETT7n96I/+6HPzuN4LOtuv/oK/0vqj+Ij1xrZ3IZJ5p5y1+Fmydrbb6/kfea48xwDcgu9IvxCKCgCAfR36d6TzE9FatpefWNXj6k+zlhaCGdfLX/anXkytzFGOLXOlOk8Zm+r5Wnl6evlD3Re25A69/K28a+JLS9ZYz91by9waQ4yp+zKulauXP/TylHrrKJVxMb4+TnPzhxyTecp8rVxr8tf9IcdM9aUt+eeUsb2YLfnhnuZeO8Bt3a2QDmVfObYV18vVsjX/3H78DK0xoT4OrTGhl2PKkvxhTc5SGTeVYy5/r79un8tTm4tfmn9Ne9m2tX+JGB9aOZfkr8eHpfFb+1OrLeyVf87o/HBm9bXq2oX7uttXO45+4d8yf+zHm9ladY6XonysR9wE6vxnMPIYy5g18fU5HZl7rVvMcaSrr5+Xo75W4/gs73PwEp32O9LxxlBuV3P19acj36Qj70gBU66plyPaR/MvkflzK+X6ev17uGf+PR7f1vgtbvH8APAynLaQjptdvV3J1dd/ZVEYHX3Oy+e1NVfdt3exVuePbU9z+eu+tY+vjo/tluq5FdNchWsVzuUyf7XjyDePkdxlTOzPFQJXfvPbo9Co4+tztjZ/rukW531JjnLMHnOuVc5ZP19z61my3jX5WrasYWS+KXvng3uK63nufRA4zmG/bFjerHoFU7bPtYWlbxQj+aOtni+U7fWYuq9W58sxvXnW6uUPW+YoY1s5o21N/jIuteLD0jWmyLNk3noNrfnrPOWYJTnK+CX5e5bGTs2fyjXHfm+N2Z/7YSp/3RfK/lKdN03lD2V/9PXytMytb64fzq5+fQD3c+hf7QDOIW68brgAsK9Xv/7rv/5VIf17v/d7T3/wB3/wfAQ8Ap9eAcAxXn3++edfFdKffvrp02efffZ8BAAA9PgnwgEO1vpe9lFuORfAS6eQBjjQrb+fHnMppgFuY/evdky9gft+5rXcugCobZk/r0PXHPdUX8Ot6zLbQraXbSn65sbWecvjNTLnaDycWe81AyMO+UQ6Lsy8OMv9EBdweRFfzdXWf8XzvXbNV3yMXMee11e+F5b5yvfKEH35vplbyv25NqCtfn3t9drm5dq9kO69mWd7r/8qrrT+8g1j9M3iHm8yuealWmPX5rgHb+DH2uv8jl5H+fq7l5h79Bxc4fUDe9jyOoHgO9IvxNqb4r2LAHhkvZt373U39Vr0WgW4n0P//F3vDT7byxtJPa7sC0tvFBnXy1/2p15MrcxRji1zpTpPGZvq+Vp5enr5Q90X1uQOkSPX18ud/ak8bvWVyr5Q94cY02oPdXzIsVPzzqlje7nq+VvzLI0NdXwvf51zyxxT5uLn1je1tjAXH6ZyrMkdls6fluSYEzlaMdle9rfaprRiW5bmK0VMmspbWzsP3EPrNTHyOoF0t0I6lH3l2K0X+tb8c/vxM7TGhPo4tMaEXo4pS/KHNTlrZWwvd2iNqfvCknxL2sLW+Dl13Nxx2CMmLc0fsq3sXxI/pTXX1HEo22I/9GLm4kOdYyq+lS+saZ/KH3q5piyZP/frn3NaOVqW5mvpxbbat8wDt1Rfq3EcXL+Mev1zP/dzT7n90R/90XPz8Y6+aG+ZP/bzxbhGneNs4jFNPa6p9Z/x8dxK/UYdjjgfU+e/duvnY4/5eo+v3B953T2S1rV2D2dYAywR12re287y+uHaXv/u7/7uU24/8zM/89x8f+WFHtvVXHn9sd54c8ntpck32pDnopbPa25nkusfXV8df8Tj3yu+tbYlyrljY7mt1xfcW1zDucFWr3/2Z3/2KbdbfiI9p7zQr3jBX339pVi7m+XX1c9tbGdSr23N8xdjy9iWsn9qXM+W+HJ9o8p5t+bqiZxz570e04up25bkjv4jHleIvOU2txaAR/X6t3/7t5++853vfLn99E//9HPz+Rz5Rj2Su4xZcsNyoznOEec2i4O55zWVa2gVFnNr3PMxbM2V6y+3OVvnHI1fGjc3bqp/62PbKubvXYdLr8893ft8wFblNTz1+oIlXn3zm9/88Ld/+7dfHnz/+99/+vM///Mv97cqL9TyIm21z7WFpRf6SP5oq+cLZXs9pu6r1flyTG+etXr5w5Y5ytipNZd6c7fiezl7OVLZH+qceVzul+r4JSJPL66eozWuXmNLue5SL3/dHuZiQ2/+lohv5SzblqxvaVvotYdy3pTjoq/cr/X6evOnsn9ujjkRm3F13lSOCb1xpTqmNNU3p5w79daWRueCe8nr2LXLVl/+1Y5Xr149vX79+ukXf/EXn37jN37juQt4iVpF2JbCjNueP88VwO28+qmf+qmv/vzdnp9IA9eVn9YkhRkAfOzVF8Xzh7//+79/+vDhw+5/RxoAAB7V1z6R/pu/+Zunv/zLv3w+AgAAer78jnR8Ih1+6Zd+ySfSAACwwKtPPvnkq0+k/8N/+PdP//bf/s3z0Zj8bmV8pzJ/6aVs4wecE86o/G5069qs++fGA8Aje/37v//7T7l985v/1XPzuN7NNNvjxlvefDm3vZ8vz/+55et07nVc67UDwCN7/eM//uNPuf3Ij/zIc/Nx9rzhnr0gm1pfnIezFx+x/lznyLluxZz9Me/p7NcnALDN6+efMOklFcCPIgp5xTwAHOfLXzZ83t/tz9/FzTs/xSx/prI91YXa0r5Uj+nJ2HL+Vmw9R46p11Ufhzo2lHO0Ympz8/fmXqqXP9R9YU3+ufjon1v/1PqW6K0h2+v92pL5yzFlf2/uUMf0jlv56vG1Mia0ci0RcVMxrfWsnQMAHsHdCulQt+Vxa3x5nHrtc+r5l8zXGhN6OUKrrdTrn8sV+6EcMzdXaS5/WpOzpRcf7aHsK8duXctcfOyH3nxz8WEkphR9IfvLsXO5luxvMZcn+kOMKfcB4KU57KsdeTPOG21t6sZb9vXityrnmFrnlDrHLd16vr3d+3zVz92a5z+v7dLI46nXcCV5zq62bgDY0yGFdH1zHbnZxk3ajfrlyuc/tzWyyBuND1vjAYDHd8pfNozCJYohRfTLlc9/ua1Rx64thuv42AAASqf/qx1LC6C1hVI5Pgv3I61d36PZ+vjXxC8ZW46J/bnnvxwfY+s55uac658yGhtxW+Yt7ZUHAB7JIb9sGOLGmwVHWaSUN+Rsn2ori5YyT5rq64mYpXlTjqnbw9TcrTmW5Fgy/1TbnF7+sGR9S2WuteufWt+cufVHfxznuFbuJfOXY3rrq+eo84YluXtxZf7eXK38Pa15Umu+1rwA8FK8+t73vvdVIf2tb33r6dvf/vbz0ePKAoCXyfMPAOzh9SeffPKU2ze+8Y3n5seVn57lT14Wzz8AsJdXH77wvP/09u3bp3fv3j0fAQAAPf6JcAAAGKCQBgCAAbt/tWPqu6d+wYtH17v+62s/x135NRGP4azrf4TzC1Nc43AOh/3LhvniLvdDvPjzDeCKrrb+q5/vM1hzDuvrPrdHeg5cU3Bf8fp7xPcWuKLdC+l4Ybdke6//Kq60fm+2+zjiOc/nZcpZn7Mla7+3K6wRRuT7eop97+9wP74j/UIoKgAA9nXon7+r/8s5ZXv5X9H1uPq/sJcWghnXy1/2p15MrcxRji1zpTpPGZvq+Vp5enr5Q90X1uROU2vrzZ/tcdyKr9t6c8zlD2viw5L8aS5+ypq5WznXrj0tWV89d28tc2sMMabuy7hWrl7+0MtT6q2jVsb2Yrbkh3uKa7f1enH9wn3crZAOZV85dusbxdb8c/vxM7TGhPo4tMaEXo4pS/KHNTlrdWx5PDdX7Iel8XPHoTUmtHLO5auPw1R8aLX1zOVKczl7/XX7XJ7aXPzS/Gvay7at/Uv1YvbKD/fg+oVzudtXO45+0d8yf+zHG9ladY6zaL0pr13fLR7PWc/f0crHesQNtM5/Bkc/vy/p+gFgP6f9jnTcwMvtaq6+/iNF0ZLnJH62ipjy3OXYpTJ/bnvnX6N8rHuLvK3HNqdcUy9HtI/mXyLz51bK9fX6tzo6PwAvx2kL6bjZ1duVXH3997bl/EVhNBdX9k+Ne4nK83eU8ry35qr79i52j84PwMtwmb/aceSNbiR3GRP7cTOecqUbdauw2Hv9OcfceUtr5s/c5TZnasyS+Cmt87lWHR/H5blbm3/p+d+67rAkRzlmjzmnHJ0fjlS/n8T+3OsYOM5hv2xYvtB7N/xsn2sLS98oRvJHWz1fKNvrMXVfrc6XY3rzrNXLH46Yo45f8vim2kK099a1JH+qc7Ty1m29/Knsj748rsfV6rhUxtdzh17e1ryt+DC3tlrkWTJvvYbW/HWecsySHGX8kvxT5uK35oczqF9TwH0c+lc74B7iBlPfXFptAABbKKR5SPWnjopoAGBvCmkAABjgnwgHAIABCmkAABiw+1c7Wr8Rn3xP9Rpaz+ERz93ULwDmGkbn3Rq/xNT6b2HL/Lc4P1d29fPj+T2W88sVTN0j5q5h1/hyh3wiHSc+T365H+LJySfoiq62/pH1Tj1/e7jaOTybtefv7Of77OvjPFwrMG/udRJ9eW9vjZvr5+t2L6R7RVe29/qv4krrP+uLIdc0ZcmYKUvi9zgn9ziva89Na+zaHEcaXceR537J+bn3a2pq/jM9vz0j5+8sj+kK55eXa+r6jNdd2Rf75Wtxrp+P+Y70C9F7UTGmfrMBAF6eQ//8Xa/YyPbyv3LqcfV/AS0tWjKul7/sT72YWpmjHFvmSnWeMjbV87Xy9PTyh7ovrMmdIk8d18qdysdT77e08odyjl5s6I2biy/709Q8Lbn2/JnquXvHrb5S2Rfq/hBjWu2hjg85dmreORlbxpVtvf1auYbUyhnq+LnYUI9praFnau6wZf46d2+uqTXMzT8Vm+ocOS7b43jLGqYsiZ2bO9t64+o56vFxPDXHlCVx9fxhzRywh7gO6+turm1pDD90t0I6lH17PpFb88/tx8/QGhPq49AaE3o5pizJH9bkbOnFl+2t/fgZWmNKvfY01V/3tcZuyT+njO3NHVpj6r6wJN+StrA1fs5crtgP5XE5fkn83Piwpr03dspczJb54zhkW9nfGlvnC732tCaubIv9UI6p+6fil+rF1O1Tx60cc22xH3o5l+rFzM0Pt7L0WizbXL/r3e2rHUc/KbfMH/txoa1V53gke5yfntaL+h7nL9Yx9bjqc1C6x3pvqX7sa57/MnbP6+Zs6nOUzvD4yzUssXb8lKWPP/pG591zvUvcej7gdk77Hel4kyy3q7n6+umL5zNujLmxv3zdjJ7fjM/t1rbOn3FnvL5iTeVji21vmbf3+OfmzPjcbukW5wc4j9MW0vFmVG9XcvX1s1w8t26W+4lzufU1k/Hldktb5t/j8R+tfFyx7Xn9L3n8c/Nmf7ndUj33nucHOJfL/NWOI9+IRnKXMbE/90b90t5I156fNVo3pq3n95Gfn6MfWyt/2Rb7U8//1PqWrn1u3NI8o46af6/Hv6cj5urlnJtraZF69fMDe6lfM7Ffvj/P9fOxw37ZsHwiyieh1T7XFpY+kSP5o62eL5Tt9Zi6r1bnyzG9edbq5Q9b55iKL/uiLY/L/VD3labyh7n+VM+XlsaH3hp7WnO25iv11taK7+Xs5Uhlf6hz5nG5X6rj58zlr9tqU/Ghzpf7tV5fmT+0Ynvq2NCLXzv/0txl3t4codW3ZI56TPaX7b22JfmXyly99ZXz1XO3jlPZHrKvNb6Xo6fOHabmDkvywl6WXIM5pndtzvXzQ4f+1Q5epngBevG9XJ5/AF4K/yALu8r/is2fvCyefwBeEp9IAwDAAJ9IAwDAAIU0AAAM2P2rHVPfjfQLSNdRPo/l89Z7fm/93OY6XFM8ovp15joHOKdDPpGON/184y/3Q9wgesXYFVxt/SPrjfH5vMVWxudzWfbXY9LaeYGPX3+91xcA97d7IR1v+i3Z3uu/iiutv7whL70RZ0xpTfyt5OMCALgX35F+IW5VdEbBXRfdrTYAgKs79M/fRfHUKuCyvSyu6nF14bW0EMy4Xv6yP/ViamWOcmyZK9V5ythUz9fK09PLH+q+sDR3xLbGlu2tMXVbuYbW2BR9eVyP65nKncoxaWl+uLe4fvN6LfcBOJe7FdKh7Ju7cay5mWzNP7cfP0NrTKiPQ2tM6OWYsiR/WJMz9WLK9tivteZOS/K1xszpxbXaR+eAe4lrNrhuAc7rbl/tOPrmcMv8sZ83vTXqHFcS6y23FOehLFrjZ7aVyvYy/ii3mAP2kq+LfJ0AcE6n/Y50Flm5Xc3V1z8qb/6lVtuRsvh4ieef64vrtXy95PUMwPmctpDO4qvcruTq67+6+twrRACAvV3mr3YcWQiN5C5jYn+uUL5KIdcqOpc8vpapmMzZmm+rq5xrAODaDvtlw7KYKQuqVvtcWyhzTBnJH231fKFsr8fUfbU6X47pzbNWL3/YY44yRy/3yLpD5oj4kXxzj2+uH86uvoZdvwDndOhf7QAAgEflH2QBAIABCmkAABigkAYAgAEKaQAAGLD7Lxu2/mJC8pvnLJXX0b2umZc+/x62Poa5eP2P3Q9wBYd8Ih1vjPnmWO6HePPMN1DgMcVrPF/7I6/3uXj9j90PcBWH/vm7fLOs9dphDdfRObWelzXP1Vy8/sfuB7gS35EGAIABdy2k41OI3GplX6t/ian4sq/sL4+n+rOtPk5le9lXt9XHqWxf0p/K41Z/aaq/7Gv1L9GKr9vq49BqK5V9vXHZXvfXbfVxaLXVyjHluPK41b/EkrhyzNxYAOAYdyuk48Yf/ysvt7IQqPvq/iVG88d+6MVnf+9nmMsfWyjHler4UI6Zyx/qMdmfpvrrvrp/iV7+PA7lmFKrrVT2t8ZOrT+Pw+j8c/lDPSb7l8iYntb8AMDt3a2QPvLmn4VGae18tyhOyjn2nm8q3x7nZ8rS/GXbnvMvdeT8t3489zh/APDSnfY70lGMlduZRNGSa4qfrSKmXHuOXSrz57Z3/iWOzn+0q69/Sn19PNrjA4CrOG0hHcVCvV3JlvVHYTQXV/ZPjRt1dP6jXX39c+rHppgGgNs7bSFdW1MotAqLvQuNnCN+LjGy/nKbM5K/NBd/dP6t9lz/2Z39sdTPf+yXr5M4nnoMc/H6H7sf4EoO+zvS5Rtl+SbZap9rCyNvtL01hF7+pesL0d5b15L8qc7Rylu3Lck/1RZ67aGXf425taS5uUNv/hy7dP17zb8k/1TblLn5l6zvDHKd9dp67bW5cfofux/gCg79B1kYEzeY+ubSagMA4H4U0ieVn9YkRTQAwLkopAEAYMBlftkQAADORCENAAADFNIAADBAIQ0AAAMU0gAAMEAhDQAAAxTSAAAwQCENAAADFNIAADBAIQ0AAAMU0gAAMEAhDQAAAxTSAAAwQCENAAADFNIAADBAIQ0AAAMU0gAAMEAhDQAAA159+MLz/tPbt2+f3r1793w05v379897H3vz5s3z3v3lOs+0prXiMVx5/QAAV3bIJ9JR3GWBV+6HKP6mim3mOYcAAPe3eyHd+4S0LKzPoC7wr+TKawcAeBS+I02XT74BAPp2/450KYqw1ien2V4WafW4uoBb+wnsVPzUvKmOD70cIfuyPY5b89RtrTGh116KMXVfxrVy9fKH1hytOAAAfuBuhXSoC7s8bsX1crUsje/lrNvnjkPZFvuhFzOXb+44rWkv2+b6AQCYd7evdjx60bbl8ZWxUeACAHA+p/2OdBSQ5bbWlvgoZMvYVlFc9se2RuYP8XMqf6tvD5k/NwAA1jltIR0FZL2tsSU+C9ipuLJ/atyIcv6jlOs+ei4AgEd0mb/asfVT0zXxUVTG+HKbsyZ/yDnmCti1eVtG1x9te8wPAPCIDvtlw7IAK4vFVvtcW5grOGtT8XVfqPvr+eq2Xv6yfaottOZJGRP95X4o86U6Tz3nXI46PtQxAAD80KF/teOqooBsFaYKSgAAkkK6Y8kntgAAvFwKaQAAGOCfCAcAgAEKaQAAGLD7Vzvq7xaXzvQ9Y3+RAgCALQ75RDqK0yxQy/0QBexUsQ0AAFeweyHd+4S3LKzPoC7wH5H/YAEAOI7vSHMY//cBAHhkh/75uyiiWp/6ZntZZNXj6gJs7afHU/FT86Y6PvRyhOzL9jhuzVO3tcaEXv7S0thQx/fy1zn3mKNuBwB4BHcrpENdmOVxK66Xq2VpfC9n3T53HMq22A+9mLl89XHYIyYtzR+yrexfEg8A8Oju9tWORy+6jnx8raL1iPnKnHP5j3y8AABndNrvSEexWG5rbYmPorCMbRWJZX9sa2T+ED/3zn+0+vycbX0AALdw2kI6irV6W2NLfBSGc3Fl/9S4UUfn36pem2IaAHhpLvNXO7YWamviszAstzlr8oecI34uUeZvFa5z869d35SluWLcnvMCAJzJYb9sWBZQZbHYap9rC0sLzjQVX/eFur+er27r5S/bp9pCa57Uy1/q5S3lmLq/l79uD3OxYWp9vbUBAFzZoX+146qiAGwVjwpCAACSQrqj/tRVEQ0AQEkhDQAAA/wT4QAAMEAhDQAAAxTSAAAwQCENAAADFNIAADBAIQ0AAAMU0gAAMEAhDQAAAxTSAAAwQCENAAADFNIAADBAIQ0AAAMU0gAAMEAhDQAAAxTSAAAwQCENAAADFNIAADDgkEL6/fv3z3tckecPAGDeqw9feN5/evv27dPP//zPPx993Zs3b573lolibG0MH8ui9tbn8uzP373Oy6PYev6OPP9nv/YAIH30iXTewOJnueWN8+xinVdZK+fgmjmXK73fAPCyzX61I29ocXM7m9bN9ozr3CL/Q+YI9y5Wtsy/53l5tGtmiSXnb+r52fP8t0Tue1+fADBn03ek40bXu9m5EV6b5497cw0CcHYffUf63bt3H928ep885bip/rqvjGnFl3Nne91WHqc6Rz2u7A91jnquXnyrv8xdx7RyhLI9lH2pFxu2xNexoRc/N2+pN8eSHDmmjmvlmcodejnKseWYNNW/ZJ5SL741b4ox2V/v13r5U2ue1hqW5A7luLn40Ftftsfxkjwhxk31A8C9dAvp8sY3ehPrxeYNtDdHK25pW6jzh3LsXK4l8WEuX2iN6Y2v21LdtyR+JKbW6y/bW/t13JI8tegLde7SXHwvdi7XXH+YOp6Ln9uPn6E1JtTHoZWntLStNNo/N1fsh3LM6FwAcE+H/h3puPHlTbNW3hTrcfXxyE107fjaXHzZX6831WNupXW+RubvPa455Vwj8anMs3b9ufbWudhqr/M7pcyXj2WLvde31Zr1HPEcAsAeZgtpN7BzyiIxt7PJdV31+inPbWxXEuf8yusvxdq9BwFwVod+Ih3ypr5WWQy4kX4szkm9HSHyrn3+8jk7ak23kOsvtyup1z7yGrw3r30Azu6wv9oxp4zb44Y5uo5Re69/q3I9rcJp7vzM9Y9amnfv+SNfnIfWuagtmbscM3J+1yrzxf6a62vvtYQjck5Z+5gB4B4m/2XDuRtZ3lyXjCvH5PFc/JKbaZ0jj8PStrAmPo7zZ7alOm8o+1M9rjUm5XylJfHlmF7+HLN0/jpnGb9kv1b35XGpjpsb05o7tMaEclyOqecoY1Mvd+jFT8071Vfr5Q91X1jTX2qtYUl8b31le68tjut8AHBGzb/acYTy5rj0RnnWG+rR66rzn+E8nPW5eETONQBcw+Hfka5FkVD+bIm+sxYTS9a/hzwHZz0PHONW1xcAsN3NPpEGAIBHcvNPpAEA4BEopAEAYIBCGgAABiikAQBggEIaAAAGKKQBAGCAQhoAAAYopAEAYIBCGgAABiikAQBggEIaAAAGKKQBAGCAQhoAAAYopAEAYIBCGgAABiikAQBggEIaAAAGKKQBAGCAQhoAAAYopAEAYIBCGgAABiikAQBggEIaAAAGKKQBAGCAQhoAAAYopAEAYIBCGgAABiikAQBggEIaAAAGKKQBAGCAQhoAAAYopAEAYIBCGgAAVnt6+v8BFHOHpZCjqD4AAAAASUVORK5CYII=
用arpack包求解实对称矩阵的特征值和特征向量。
如图,报如上错误,不知如何解决,代码和输入数据在附件,个人怀疑脚本有问题
错误:“ Error with _seupd, info =            14445712
Check the documentation of _seupd.”
页: [1]
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