I am using LAPACK library to solve an optimization problem. I am getting different Lapack errors (Lapack error(1,2,3,4) with SGETRF). Is there any documentation source, which I can read to understand what exactly these errors mean?
Most of Lapack routines return an error code in the argument INFO. The meaning of this output is specified in the documentation of the function sgetrf()
If INFO is null, then everething is OK !
If INFO <0 , an error was detected in the input parameters. For instance, for sgetrf():
INFO=-1 : number of rows of the matrix is negative
INFO=-2 : number of columns of the matrix is negative
INFO=-4 : LDA is lower than MAX( 1, M )
The routine xerbla() can be applied to print the right error message:
XERBLA( 'SGETRF', -INFO )
If INFO>0, the document clearly describes what is happening :
U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, and division by zero will occur if it is used
to solve a system of equations.
This problem can occur if the matrix is singular or ill-conditionned. If the matrix A is singular, there might be different solutions, or no solution at all to the problem Ax=b.
If INFO=1, it is likely that the matrix is null.
Hence, testing INFO is to be considered as a good practice as functions such as sgetrf() or sgesv() are called...
Related
I used scipy.optimize.fmin_bfgs to minimize the hinge loss (SVM). However, there are errors :
Divide-by-zero encountered: rhok assumed large.
Somebody said that “It had to do with the training data set”， anybody knows how to deal with the problem?
From the source code of scipy, rhok is,
rhok = 1.0 / (numpy.dot(yk, sk))
where both yk and sk depend on intput array x0.
A possible causes of this error may be a bad choice of initial condition x0 which tends to singularities in your function f. I would suggest plotting your function and maybe ensuring initial conditions are always away from possible divergent values. If this is part of a larger training routine, you could possibly use try and on catching an ZeroDivisionError try shifting the initial condition shifted by some amount. You may also find a different minimisation method is more robust from scipy minimize.
If you add the full_output option to scipy.optimize.fmin_bfgs it should give you more information about you particular case.
I've just updated to Matlab 2014a finally. I have loads of scripts that use the Symbolic Math Toolbox that used to work fine, but now hit the following error:
Error using mupadmex
Error in MuPAD command: Division by zero. [_power]
Evaluating: symobj::trysubs
I can't post my actual code here, but here is a simplified example:
syms f x y
f = x/y
results = double(subs(f, {'x','y'}, {1:10,-4:5}))
In my actual script I'm passing two 23x23 grids of values to a complicated function and I don't know in advance which of these values will result in the divide by zero. Everything I can find on Google just tells me not to attempt an evaluation that will result in the divide by zero. Not helpful! I used to get 'inf' (or 'NaN' - I can't specifically remember) for those it could not evaluate that I could easily filter for when I do the next steps on this data.
Does anyone know how to force Matlab 2014a back to that behaviour rather than throwing the error? Or am I doomed to running an older version of Matlab forever or going through the significant pain of changing my approach to this to avoid the divide by zero?
You could define a division which has the behaviour you want, this division function returns inf for division by zero:
mydiv=#(x,y)x/(dirac(y)+y)+dirac(y)
f = mydiv(x,y)
results = double(subs(f, {'x','y'}, {1:10,-4:5}))
I have to carry out the following operation
R=[0,0.5,-0.25;-0.25,0,0.25;0,0,0.25];
B=[0,k21,k31;k12,0,k32;0,0,k];
G=inv(R).*B;
g=det(G);
but Matlab is showing the following error
??? Error using ==> horzcat
CAT arguments dimensions are not consistent.
Error in ==> g at 60
B=[0,k21,k31;k12,0,k32;0,0,k];
K21,K31,K12,K32 and k all have dimensions of 923334 by 1. Can anyone help me how can I carry out the following operation.
Your code works well for me. Check that the k-values (k12,k31,k32...) are scalars (or 1x1 dimension)
EDIT :
For the case you mention, k's are nx1, one simple way is to perform a loop:
R=[0,0.5,-0.25;-0.25,0,0.25;0,0,0.25];
for ii=1:length(k)
B=[0,k21(ii),k31(ii);k12(ii),0,k32(ii);0,0,k(ii)];
G=inv(R).*B;
g(ii)=det(G);
end
There is also a "vectorized" way to do that, but it seems to be good enough...
I am working on a project that needs to use hidden markov models. I downloaded Kevin Murphy's toolbox. I have some problems about the usage. In the toolbox webpage, he says that first input of dhmm_em and dhmm_logprob are symbol sequence data. On their examples, they give row vectors as data. So, when I give my symbol sequence as row vector, I get error;
??? Error using ==> assert at 9
assertion violated:
Error in ==> fwdback at 105
assert(approxeq(sum(alpha(:,t)),1))
Error in ==> dhmm_logprob at 17
[alpha, beta, gamma, ll] = fwdback(prior,
transmat, obslik, 'fwd_only', 1);
Error in ==> mainCourseProject at 110
loglik(train_act) =
dhmm_logprob(orderedSymbols,
hmm{train_act}.prior,
hmm{train_act}.trans,
hmm{act}.emiss);
However, before giving this error, code works for some symbol vectors. When I give my data as column vector, functions work fine, no errors. So why exactly am I getting this error?
You might say that I should be giving not single vectors, but vector sets, I also tried to collect my feature vectors in a struct and give row vectors as such, but nothing changed, I still get assertion error.
By the way, my symbol sequence does not have any zeros, I am doing everything almost the same as they showed in their examples, so I would be greatful if anyone could help me please.
Im not sure, but from the function call stack shown above, shouldn't the last line be hmm{train_act}.emiss instead of hmm{act}.emiss.
In other words when you computing the log-probability of a sequence, you should pass components that belong to the same HMM model (transition matrix, emission matrix, and prior probabilities).
By the way, the ASSERT in the code is a sanity check that a vector of probabilities should sum to 1. Oftentimes, when working with very small values (log-probabilities), numerical stability issues can creep in... You could edit the APPROXEQ function to relax the comparison a bit, by giving it a bigger margin of error
This error message and the code it refers to are human-readable. An assertion is a guard put in by the programmer, to ensure that certain conditions are met. In this case, what is the condition? approxeq(sum(alpha(:,t)),1) I'd venture to say that approxeq wants the values to be approximately equal, so this boils down to: sum(alpha(:,t)) ~= 1
Without knowing anything about the code, I'd also guess that these refer to probabilities. The probabilities of a node's edges must sum to one. Hopefully this starts you down a productive debugging path. If you can't figure out what's wrong with your input that produces this condition, start wading into the code a bit to see where this alpha vector comes from, and how it ended up invalid.
I'm conducting dimensional reduction of a square matrix A. My issue now is that I have problem computing eigvalue decomposition of a 13000 x 13000 matrix A, i.e. [v d]=eigs(A). Because it's a sparse matrix, I get 'out of memory error' using a 4GB RAM. I'm convinced it's not my PC's problem, since the memory is not used up when eigs command is run. The help I saw online had to do with ARPACK. I checked the recommended site, but there were a lot of files there, don't know which to download. Also, I did not understand how to use it with MATLAB. Another help says use numerical methods, but I dont know which specific one to use. Please any solution is welcome.
Error in ==> eigs>ishermitian at 1535
tf = isequal(A,A');
Error in ==> eigs>checkInputs at 479
issymA = ishermitian(A);
Error in ==> eigs at 96
[A,Amatrix,isrealprob,issymA,n,B,classAB,k,eigs_sigma,whch, ...
Error in ==> labcomp at 20
[vector lambda] = eigs(A)
Please can I get translation of these errors and how to correct it?
The reason you don't see the memory used up, is that it isn't used up - Matlab fails to allocate the needed amount of memory.
Although an array of 13000 x 13000 doubles (the default data type in Matlab) is about 1.25 GB, it doesn't mean a 4Gb of ram is enough - Matlab need 1.25Gb of contiguous memory, otherwise it won't succeed in allocating your matrix. You can read more on memory problems in Matlab here: http://www.mathworks.com/support/tech-notes/1100/1106.html
You can as a first step try using single precision:
[v d]=eigs(single(A));
You say
another help says use numerical methods
If you are doing it on the computer, it's numerical by definition.
If you dont' want (or can't due to memory constraints) to do it in Matlab, you can look for a linear algebra library (ARPACK is just one of them) and have the calculation done outside of Matlab.
First if A is sparse, single(A) won't work. Single sparse matrices are not implemented in MATLAB, see comments:
how to create a single float sparse matrix in mex files
The call to ishermitian may fail because you can't store two copies of your matrix (A and A'). Bypass this problem by commenting the line out and setting issymA to true or false, depending on whether your matrix is Hermitian.
If you find further problems with memory inside eigs, try to reduce its memory footage by asking less solutions, eigs(A,1), or reducing the maximum size of the basis (option p), which by default is twice the number of asked solutions:
opts.p = 3
[x,d] = eigs(A,2,'LM',opts)