Displaying 20 results from an estimated 9000 matches similar to: "SVD with positivity constraints"
2012 Dec 05
1
Understanding svd usage and its necessity in generalized inverse calculation
Dear R-devel:
I could use some advice about matrix calculations and steps that might
make for faster computation of generalized inverses. It appears in
some projects there is a bottleneck at the use of svd in calculation
of generalized inverses.
Here's some Rprof output I need to understand.
> summaryRprof("Amelia.out")
$by.self
self.time self.pct
2005 Dec 02
5
what is best for scripting?
I am using R in Windows. I see that I will have to use batch processes with
R. I will have to read and write text files, and run some R code; probably
some external code too. I have never done scripting. Is there any document
that explains simple steps with examples? I also have heard that Python is a
good scripting language. Is it worth the effort? (I do not have too much
free time, so if I could
2005 Jul 21
1
again, a question between R and C++
Dear R Users,
I want to make a call from R into C++. My inputs are List1, List2, List3,
IntegerID. The amount of elements of the lists and their type depend on
IntegerID. Typical elements of a given list can be vectors, doubles, and
even other lists. I want to return also a list (whose nature will depend
also, possibly, on IntegerID).
What I want to do is to call these 4 inputs from C++ and then
2005 Sep 23
4
books about MCMC to use MCMC R packages?
Dear list users,
I need to learn about MCMC methods, and since there are several packages in
R that deal with this subject, I want to use them.
I want to buy a book (or more than one, if necessary) that satisfies the
following requirements:
- it teaches well MCMC methods;
- it is easy to implement numerically the ideas of the book, and notation
and concepts are similar to the corresponding R
2005 Jul 24
4
problem building R packages in windows xp
Dear R users,
I am having problems building R packages in Windows xp. I have followed the
instructions from Peter E. Rossi in Documentation -> Other, except for the
TeX version (fpTeX), since when I go to the recommended webpage, it is said
that fpTeX has been discontinued. I have MikTeX in my computer, and I have
followed the recommendations in
2012 Dec 12
3
R-2.15.2 changes in computation speed. Numerical precision?
Speaking of optimization and speeding up R calculations...
I mentioned last week I want to speed up calculation of generalized
inverses. On Debian Wheezy with R-2.15.2, I see a huge speedup using a
souped up generalized inverse algorithm published by
V. N. Katsikis, D. Pappas, Fast computing of theMoore-Penrose inverse
matrix, Electronic Journal of Linear Algebra,
17(2008), 637-650.
I was so
2000 Apr 28
3
Matrix inverse
I haven't found a function that directly calculates the matrix inverse, does it exist? Otherwise what would be the fastest way to do it?
Patrik Waldmann
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2004 Oct 01
1
cumsum over a list or an array
Hello list,
my question is related to svd of a matrix:
b=matrix(rnorm(50),10,5)
mysvd=svd(b)
I would like to compute each xi where xi = di* ui %*% t(vi). I do it by :
xlist=sapply(1:ncol(b), function(x1,y)
y$d[x1]*y$u[,x1]%*%t(y$v[,x1]),y=mysvd,simplify=F) # result is a list
xarray=array(sapply(1:ncol(b), function(x1,y)
y$d[x1]*y$u[,x1]%*%t(y$v[,x1]),y=mysvd),c(nrow(b),ncol(b),ncol(b))) #
2002 Apr 01
3
svd, La.svd (PR#1427)
(I tried to send this earlier, but it doesnt seem to have come through,
due to
problems on my system)
Hola:
Both cannot be correct:
> m <- matrix(1:4, 2)
> svd(m)
$d
[1] 5.4649857 0.3659662
$u
[,1] [,2]
[1,] -0.5760484 -0.8174156
[2,] -0.8174156 0.5760484
$v
[,1] [,2]
[1,] -0.4045536 0.9145143
[2,] -0.9145143 -0.4045536
> La.svd(m)
$d
[1]
2008 May 16
1
Dimensions of svd V matrix
Hi,
I'm trying to do PCA on a n by p wide matrix (n < p), and I'd like to
get more principal components than there are rows. However, svd() only
returns a V matrix of with n columns (instead of p) unless the argument
nv=p is set (prcomp calls svd without setting it). Moreover, the
eigenvalues returned are always min(n, p) instead of p, even if nv is set:
> x <-
2000 Aug 10
1
svd error (PR#631)
--=====================_24736660==_
Content-Type: text/plain; charset="iso-8859-1"; format=flowed
Content-Transfer-Encoding: quoted-printable
SVD-Error on
R 1.1.0
Windows 98
I get the following error applying svd on a positive definite matrix :
> sk2
[,1] [,2] [,3] [,4] [,5]
[1,] 1.0460139783 0.084356992 -2.810553e-04
2008 Apr 15
1
SVD of a variance matrix
Hello!
I suppose this is more a matrix theory question than a question on R,
but I will give it a try...
I am using La.svd to compute the singular value decomposition (SVD) of
a variance matrix, i.e., a symmetric nonnegative definite square
matrix. Let S be my variance matrix, and S = U D V' be its SVD. In my
numerical experiments I always got U = V. Is this necessarily the
case? Or I might
2010 Sep 22
3
eigen and svd
Dear R-helpers,
could anybody explain me briefly what is the difference between
eigenvectors returned by 'eigen' and 'svd' functions and how they are
related?
Thanks in advance
Ondrej Mikula
2013 Apr 08
3
SVD on very large data matrix
Dear All,
I need to perform a SVD on a very large data matrix, of dimension ~ 500,000 x 1,000 , and I am looking
for an efficient algorithm that can perform an approximate (partial) SVD to extract on the order of the top 50
right and left singular vectors.
Would be very grateful for any advice on what R-packages are available to perform such a task, what the RAM requirement is, and indeed what
2005 Jan 27
2
svd error
Hi,
I met a probem recently and need your help. I would really appreciate
it.
I kept receiving the following error message when running a program:
'Error in svd(X) : infinite or missing values in x'.
However, I did not use any svd function in this program though I did
include the function pseudoinverse. Is the problem caused by doing
pseudoinverse?
Best regards,
Tongtong
2010 Jan 16
2
La.svd of a symmetric matrix
Dear R list users,
the singluar value decomposition of a symmetric matrix M is UDV^(T), where U = V.
La.svd(M) gives as output three elements: the diagonal of D and the two orthogonal matrices u and vt (which is already the transpose of v).
I noticed that the transpose of vt is not exactly u. Why is that?
thank you for your attention and your help
Stefano
AVVISO IMPORTANTE: Questo messaggio di
2001 Nov 02
1
Look, Watson! La.svd & ATLAS
Dear R-devel,
I had attempted to compile r-devel (dated Oct. 31, 2001) on WinNT with link
to ATLAS, with mostly success. However, when I tried the following, I got a
visit from Dr. Watson:
R : Copyright 2001, The R Development Core Team
Version 1.4.0 Under development (unstable) (2001-10-31)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under
2011 Sep 13
1
SVD Memory Issue
I am trying to perform Singular Value Decomposition (SVD) on a Term Document
Matrix I created using the 'tm' package. Eventually I want to do a Latent
Semantic Analysis (LSA).
There are 5677 documents with 771 terms (the DTM is 771 x 5677). When I try
to do the SVD, it runs out of memory. I am using a 12GB Dual core Machine
with Windows XP and don't think I can increase the memory
2001 Sep 06
1
svd and eigen
Hello List,
i need help for eigen and svd functions. I have a non-symmetric
square matrix. These matrix is not positive (some eigenvalues are
negative). I want to diagonalise these matrix. So, I use svd and
eigen and i compare the results. eigen give me the "good" eigenvalues
(positive and negative). I compare with another software and the
results are the same. BUT, when i use svd,
2002 Dec 03
2
missing values and svd
Dear All,
Is it possible to manage a svd analysis within a matrix containing NA
values. If not how do I could overcome this problem.
Thanks in advance
Antonio