Python Error Singular Matrix

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Numpy Inverse Singular Matrix

million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Numpy error: Singular matrix up vote 3 down vote favorite 1 What does the error Numpy error: Matrix is

Raise Linalgerror("singular Matrix")

singular mean specifically (when using the linalg.solve function)? I have looked on Google but couldn't find anything that made it clear when this error occurs. python numpy share|improve this question edited Jan 13 '15 at 8:23 strpeter 381522 asked Dec 10 '12 at 5:47 KaliMa 5271620 If you have a singular matrix, then it might indicate that you have some mistake in your matrix filling routine. If your matrix really is numpy solve singular matrix singular, then you may get some useful information about it using singular value decomposition. However in this case you need to have a good understanding of linear algebra and numerical computing concepts. –DaveP Dec 14 '12 at 4:56 add a comment| 1 Answer 1 active oldest votes up vote 8 down vote accepted A singular matrix is one that is not invertible. This means that the system of equations you are trying to solve does not have a unique solution; linalg.solve can't handle this. You may find that linalg.lstsq provides a usable solution. share|improve this answer answered Dec 10 '12 at 6:09 Michael J. Barber 15.9k63564 By "no unique solution" do you mean it may have multiple solutions? –KaliMa Dec 10 '12 at 6:37 @KaliMa That is what I mean. –Michael J. Barber Dec 10 '12 at 7:28 @MichaelJBarber Is there a way to get it to return all possible solutions? Is that what linalg.lstsq does? –KaliMa Dec 10 '12 at 14:07 1 @KaliMa When a system of equations is singular, it either has infinitely many solutions, or none - so no, in general you can't retrieve them all. Linalg.lstsq just returns one of those solutions - even if there is none: in that case, it returns the 'best'

here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack linalgerror python Overflow the company Business Learn more about hiring developers or posting ads with us Stack linalg.solve python Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community python determinant of 6.2 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Singular matrix issue with Numpy up vote 2 down vote favorite 1 I am trying to multiply http://stackoverflow.com/questions/13795682/numpy-error-singular-matrix a vector(3 by 1) by its transpose(1 by 3). I get a (3 by 3) array but I cannot get its inverse. Any idea why? import numpy as np c=array([1, 8, 50]) np.transpose(c[np.newaxis]) * c array([[ 1, 8, 50], [ 8, 64, 400], [ 50, 400, 2500]]) np.linalg.inv(np.transpose(c[np.newaxis]) * c) Traceback (most recent call last): File "", line 1, in File "C:\Python26\lib\site-packages\numpy\linalg\linalg.py", line 445, in inv return wrap(solve(a, identity(a.shape[0], dtype=a.dtype))) http://stackoverflow.com/questions/10326015/singular-matrix-issue-with-numpy File "C:\Python26\lib\site-packages\numpy\linalg\linalg.py", line 328, in solve raise LinAlgError, 'Singular matrix' LinAlgError: Singular matrix python matrix numpy share|improve this question edited Apr 26 '12 at 21:43 bmu 13k25283 asked Apr 26 '12 at 1:17 Neerav 48221020 add a comment| 2 Answers 2 active oldest votes up vote 15 down vote By definition, by multiplying a 1D vector by its transpose, you've created a singular matrix. Each row is a linear combination of the first row. Notice that the second row is just 8x the first row. Likewise, the third row is 50x the first row. There's only one independent row in your matrix. share|improve this answer answered Apr 26 '12 at 1:21 Joe Kington 121k17279296 That was just plain stupid of me..thanks for you help... –Neerav Apr 26 '12 at 13:26 add a comment| up vote 15 down vote The matrix you pasted [[ 1, 8, 50], [ 8, 64, 400], [ 50, 400, 2500]] Has a determinant of zero. This is the definition of a Singular matrix (one for which an inverse does not exist) http://en.wikipedia.org/wiki/Invertible_matrix share|improve this answer answered Apr 26 '12 at 1:22 kaveman 2,7241434 add a comment| Your Answer draft saved draft discarded Sign up or log in Sign up using Google Sign up using Facebook Sig

here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this http://stackoverflow.com/questions/9155478/how-to-try-except-an-illegal-matrix-operation-due-to-singularity-in-numpy site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x http://www.gossamer-threads.com/lists/python/python/753303 Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up How singular matrix to try-except an illegal matrix operation due to singularity in NumPy up vote 8 down vote favorite 1 In NumPy, I'm trying to use linalg to compute matrix inverses at each step of a Newton-Raphson scheme (the problem size is small intentionally so that we can invert analytically computed Hessian matrices). However, after I get far along towards convergence, the Hessian gets close to linalgerror: singular matrix singular. Is there any method within NumPy that lets me test whether a matrix is considered singular (computing determinant is not robust enough)? Ideally, it would be nice if there's a way to use a try except block to catch NumPy's singular array error. How would I do this? The NumPy error given at the terminal is: raise LinAlgError, 'Singular matrix' numpy.linalg.linalg.LinAlgError: Singular matrix python numpy linear-algebra share|improve this question edited Feb 6 '12 at 5:06 asked Feb 6 '12 at 4:08 Mr. F 19.5k1266126 add a comment| 1 Answer 1 active oldest votes up vote 18 down vote accepted The syntax would be like this: import numpy try: # your code that will (maybe) throw except numpy.linalg.linalg.LinAlgError as err: if 'Singular matrix' in err.message: # your error handling block else: raise share|improve this answer edited Feb 20 '15 at 4:35 answered Feb 6 '12 at 4:10 wim 76.9k24152241 Thanks. It's one of those head-slapping "d'oh" moments; I didn't realize we could directly use the NumPy errors in an except statement. –Mr. F Feb 6 '12 at 5:02 Also, is there any way to make t

Post #1 of 9 (4310 views) Permalink Error in linalg.inv ?? Hello, I ran the following code (Using Debian 5.0) from numpy import * a = arange(1.,10.) b = reshape(a, [3,3]) c = linalg.inv(b) print b print c print dot(b,c) print dot(c,b) And the result is [[ 1. 2. 3.] [ 4. 5. 6.] [ 7. 8. 9.]] [[ 3.15221191e+15 -6.30442381e+15 3.15221191e+15] [ -6.30442381e+15 1.26088476e+16 -6.30442381e+15] [ 3.15221191e+15 -6.30442381e+15 3.15221191e+15]] [[-0.5 -1. -1. ] [-1. -2. 2. ] [-1.5 -3. 1. ]] [[ 5.5 8. 10.5] [ 3. 0. -3. ] [ -1. 0. -3. ]] NOT the identity matrix. Any help ? Thanks Ajith Kumar -- http://mail.python.org/mailman/listinfo/python-list sjmachin at lexicon Jun6,2009,12:15AM Post #2 of 9 (4164 views) Permalink Re: Error in linalg.inv ?? [In reply to] On Jun 6, 3:34 pm, Ajith Kumar wrote: > Hello, >  I ran the following code (Using Debian 5.0) > > from numpy import * > a = arange(1.,10.) > b = reshape(a, [3,3]) > c = linalg.inv(b) > print b > print c > print dot(b,c) > print dot(c,b) > > And the result is > > [[ 1.  2.  3.] >  [ 4.  5.  6.] >  [ 7.  8.  9.]] > > [[  3.15221191e+15  -6.30442381e+15   3.15221191e+15] >  [ -6.30442381e+15   1.26088476e+16  -6.30442381e+15] >  [  3.15221191e+15  -6.30442381e+15   3.15221191e+15]] > > [[-0.5 -1.  -1. ] >  [-1.  -2.   2. ] >  [-1.5 -3.   1. ]] > > [[  5.5   8.   10.5] >  [  3.    0.   -3. ] >  [ -1.    0.   -3. ]] > > NOT the identity matrix. Any help ? It's a longer time than I care to divulge since I took courses in matrix algebra, but I do have a vague recollection that if determinant (B) is zero, inverse(B) is not defined ... seeing the rows and columns in B are linear (as are those of C), IIRC that means the determi