Eigenvalue problems
This script explores how to use Matlab to solve eigenvalue problems. Both the naive and 'proper' way are shown.
Contents
The Eig function
The simplest way to compute eigenvalues and eigenvectors in Matlab is to use the built in "eig" function.
A=rand(10); tic EigVal1=eig(A) toc
EigVal1 = 4.7876 + 0.0000i -0.7449 + 0.3597i -0.7449 - 0.3597i -0.3286 + 0.4946i -0.3286 - 0.4946i -0.1755 + 0.5365i -0.1755 - 0.5365i 0.1586 + 0.1528i 0.1586 - 0.1528i -0.0319 + 0.0000i Elapsed time is 0.000291 seconds.
This will produce a column vector of the eigenvalues of A. To also get the eigenvectors, return two outputs:
tic [EigVec2,EigVal2]=eig(A); toc
Elapsed time is 0.000281 seconds.
This will return the eigenvectors as the columns of EigVec, and the eigenvalues as the entries of the diagonal matrix EigVal.
Root Solving
We can also use Matlab to solve an eigenvalue problem in the direct way one would do it on paper. We can calculate the characteristic polynomial of our matrix and then find the roots. Using the built in functions charpoly and roots, this is:
tic EigVal3=roots(charpoly(A)) toc
EigVal3 = 4.7876 + 0.0000i -0.7449 + 0.3597i -0.7449 - 0.3597i -0.3286 + 0.4946i -0.3286 - 0.4946i -0.1755 + 0.5365i -0.1755 - 0.5365i 0.1586 + 0.1528i 0.1586 - 0.1528i -0.0319 + 0.0000i Elapsed time is 0.097021 seconds.
Large matrices
As you can see in the timings, this method is significantly slower than using the built in function. For large enough matrices, it is not feasible to solve the problem algebraically.
A=rand(1000); tic EigVal4=eig(A) toc tic [EigVec5,EigVal5]=eig(A); toc
EigVal4 = 1.0e+02 * 5.0013 + 0.0000i 0.0920 + 0.0148i 0.0920 - 0.0148i 0.0935 + 0.0000i 0.0730 + 0.0548i 0.0730 - 0.0548i 0.0786 + 0.0463i 0.0786 - 0.0463i 0.0813 + 0.0393i 0.0813 - 0.0393i 0.0892 + 0.0000i 0.0678 + 0.0598i 0.0678 - 0.0598i 0.0483 + 0.0785i 0.0483 - 0.0785i 0.0854 + 0.0247i 0.0854 - 0.0247i 0.0747 + 0.0480i 0.0747 - 0.0480i 0.0670 + 0.0581i 0.0670 - 0.0581i 0.0610 + 0.0642i 0.0610 - 0.0642i 0.0520 + 0.0722i 0.0520 - 0.0722i 0.0485 + 0.0747i 0.0485 - 0.0747i 0.0725 + 0.0480i 0.0725 - 0.0480i 0.0557 + 0.0672i 0.0557 - 0.0672i 0.0418 + 0.0784i 0.0418 - 0.0784i 0.0334 + 0.0825i 0.0334 - 0.0825i 0.0214 + 0.0870i 0.0214 - 0.0870i 0.0301 + 0.0829i 0.0301 - 0.0829i -0.0050 + 0.0906i -0.0050 - 0.0906i -0.0430 + 0.0817i -0.0430 - 0.0817i -0.0340 + 0.0852i -0.0340 - 0.0852i 0.0086 + 0.0880i 0.0086 - 0.0880i -0.0301 + 0.0860i -0.0301 - 0.0860i -0.0914 + 0.0118i -0.0914 - 0.0118i -0.0472 + 0.0774i -0.0472 - 0.0774i -0.0646 + 0.0637i -0.0646 - 0.0637i -0.0906 + 0.0040i -0.0906 - 0.0040i -0.0896 + 0.0000i -0.0859 + 0.0293i -0.0859 - 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0.0085i -0.0078 + 0.0015i -0.0078 - 0.0015i 0.0065 + 0.0000i -0.0043 + 0.0000i -0.0004 + 0.0000i 0.0010 + 0.0000i Elapsed time is 0.845069 seconds. Elapsed time is 1.303864 seconds.
If the matrix you are considering is very large, you may not want all of the eigenvalues, in this case you can use:
tic EigVal6=eigs(A); toc
Elapsed time is 1.893703 seconds.
This returns the largest (in magnitude) six eigenvalues of the matrix. The function works the same as the previous one to calculate eigenvectors.
tic [EigVec7,EigVal7]=eigs(A) toc
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0.0447i 0.0192 + 0.0096i 0.0192 - 0.0096i 0.0039 - 0.0221i 0.0039 + 0.0221i 0.0305 - 0.0007i 0.0305 + 0.0007i -0.0487 - 0.0324i -0.0487 + 0.0324i -0.0262 + 0.0129i -0.0262 - 0.0129i -0.0100 + 0.0330i -0.0100 - 0.0330i -0.0405 + 0.0353i -0.0405 - 0.0353i -0.0185 + 0.0356i -0.0185 - 0.0356i -0.0131 - 0.0101i -0.0131 + 0.0101i 0.0104 - 0.0054i 0.0104 + 0.0054i -0.0000 - 0.0122i -0.0000 + 0.0122i 0.0067 - 0.0246i 0.0067 + 0.0246i 0.0220 + 0.0155i 0.0220 - 0.0155i -0.0279 - 0.0117i -0.0279 + 0.0117i 0.0098 - 0.0034i 0.0098 + 0.0034i -0.0112 + 0.0515i -0.0112 - 0.0515i -0.0288 - 0.0162i -0.0288 + 0.0162i -0.0397 - 0.0070i -0.0397 + 0.0070i -0.0314 - 0.0469i -0.0314 + 0.0469i 0.0094 - 0.0181i 0.0094 + 0.0181i -0.0290 + 0.0009i -0.0290 - 0.0009i -0.0063 + 0.0083i -0.0063 - 0.0083i -0.0337 - 0.0354i -0.0337 + 0.0354i -0.0027 + 0.0091i -0.0027 - 0.0091i 0.0289 - 0.0106i 0.0289 + 0.0106i -0.0460 + 0.0048i -0.0460 - 0.0048i -0.0034 + 0.0139i -0.0034 - 0.0139i 0.0016 - 0.0139i 0.0016 + 0.0139i -0.0027 + 0.0042i -0.0027 - 0.0042i -0.0153 - 0.0099i -0.0153 + 0.0099i -0.0248 + 0.0107i -0.0248 - 0.0107i -0.0043 - 0.0036i -0.0043 + 0.0036i 0.0126 - 0.0076i 0.0126 + 0.0076i -0.0190 + 0.0338i -0.0190 - 0.0338i -0.0425 - 0.0128i -0.0425 + 0.0128i 0.0219 + 0.0171i 0.0219 - 0.0171i 0.0012 + 0.0050i 0.0012 - 0.0050i 0.0037 - 0.0237i 0.0037 + 0.0237i 0.0195 - 0.0400i 0.0195 + 0.0400i 0.0056 + 0.0370i 0.0056 - 0.0370i -0.0003 - 0.0366i -0.0003 + 0.0366i 0.0426 + 0.0197i 0.0426 - 0.0197i -0.0139 - 0.0028i -0.0139 + 0.0028i 0.0352 + 0.0240i 0.0352 - 0.0240i -0.0472 - 0.0364i -0.0472 + 0.0364i -0.0357 + 0.0350i -0.0357 - 0.0350i 0.0301 + 0.0626i 0.0301 - 0.0626i 0.0367 - 0.0208i 0.0367 + 0.0208i -0.0255 - 0.0220i -0.0255 + 0.0220i -0.0267 - 0.0158i -0.0267 + 0.0158i 0.0141 + 0.0288i 0.0141 - 0.0288i 0.0334 - 0.0246i 0.0334 + 0.0246i 0.0016 + 0.0116i 0.0016 - 0.0116i -0.0003 + 0.0106i -0.0003 - 0.0106i -0.0156 - 0.0006i -0.0156 + 0.0006i -0.0002 - 0.0070i -0.0002 + 0.0070i 0.0407 - 0.0040i 0.0407 + 0.0040i 0.0032 + 0.0155i 0.0032 - 0.0155i 0.0727 + 0.0304i 0.0727 - 0.0304i -0.0134 + 0.0229i -0.0134 - 0.0229i 0.0029 + 0.0226i 0.0029 - 0.0226i 0.0176 - 0.0073i 0.0176 + 0.0073i -0.0029 - 0.0032i -0.0029 + 0.0032i -0.0272 - 0.0385i -0.0272 + 0.0385i -0.0059 + 0.0126i -0.0059 - 0.0126i EigVal7 = 1.0e+02 * Columns 1 through 4 5.0013 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0935 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0920 + 0.0148i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0920 - 0.0148i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i Columns 5 through 6 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0483 + 0.0785i 0.0000 + 0.0000i 0.0000 + 0.0000i 0.0483 - 0.0785i Elapsed time is 1.047719 seconds.
If you specify a number, k, after the matrix, the function will return the largest k eigenvalues.
tic EigVal8=eigs(A,1) toc
EigVal8 = 500.1302 Elapsed time is 0.020651 seconds.