lapack

Error while installing package from github in R. Error in dyn.load

由 邮差的信 提交于 2019-12-07 17:38:42
问题 I am trying to install the recommenderlabrats package from github to my SUSE Linux R-Server, using the straight forward: devtools::install_github("sanealytics/recommenderlabrats") However I do get an error message which I can't get wrap my head around. Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared object '/home/ruser/R/x86_64-unknown-linux-gnu-library/3.2/recommenderlabrats/libs/recommenderlabrats.so': /home/ruser/R/x86_64-unknown-linux-gnu-library/3.2

Why do I get errors installing precompiled versions of LAPACK on Windows?

由 雨燕双飞 提交于 2019-12-07 17:22:23
问题 I am trying to use the Armadillo matrix library to do matrix calcualtions and it needs BLAS and LAPACK. The Armadillo documentation recommended getting the precompiled versions from http://www.stanford.edu/~vkl/code/libs.html There are .lib and .dll files in there. The only problem is I don't know how to get Visual Studio (Express Edition 2008) to recognize these files. I try to copy them to the Visual C++ include and lib directories C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC

Using alternative LAPACK driver in numpy's svd method?

由 北战南征 提交于 2019-12-07 13:52:56
问题 I'm using numpy.svd to compute singular value decompositions of badly conditioned matrices. For some special cases the svd won't converge and raise a Linalg.Error. I've done some research and found that numpy uses the DGESDD routine from LAPACK. The standard implementation has a hardcoded iteration limit of 35 or something iterations. If I try to decompose the same matrix in Matlab, everything works fine, and I think there's two reasons for that: 1. Matlab uses DGESVD instead of DGESDD which

memory leak in dgemm_

由 房东的猫 提交于 2019-12-07 13:01:06
问题 I am currently working on an application which involves lots and lots of calls to blas routines. Routinely checking for memory leaks I discovered, that I am loosing bytes in a dgemm call. The call looks like this: // I want to multiply 2 nxn matrices and put the result into C - an nxn matrix double zero = 0.0; double one = 1.0; double n; // matrix dimension char N = 'N'; dgemm_(&N, &N, &n, &n, &n, &one, A, &n, B, &n, &zero, C, &n); A,B and C are double fields of size n*n. The valgrind output

Configuring LAPACK in Eclipse-Photran for fortran compiler on Windows

由 非 Y 不嫁゛ 提交于 2019-12-07 11:52:15
问题 Update Thank you Vladimir for the usefull insights in libraries. I took another approach, developping first in ubuntu (which was a lot easier then messing around with Eclipse/Cygwin/... and now I'm trying to port to windows, which goes rather ok, however I have some questions about that too, posted here: Problems with porting a fortran program from ubuntu to windows Question I currently have the following setup and can't get the lapack library configured so that my fortran code can compile:

Docker images with architecture optimisation?

由 眉间皱痕 提交于 2019-12-07 09:20:07
问题 Some libraries such as BLAS/LAPACK or certain optimisation libraries get optimised for the local machine architecture upon compilation time. Lets take OpenBlas as an example. There exist two ways to create a Docker container with OpenBlas: Use a Dockerfile in which you specify a git clone of the OpenBlas library together with all necessary compilation flags and build commands. Pull and run someone else's image of Ubuntu + OpenBlas from the Docker Hub. Option (1) guarantees that OpenBlas is

How to build hmatrix with BLAS and LAPACK under Windows?

由 孤者浪人 提交于 2019-12-07 07:38:55
问题 Windows 10 x64 Cabal 1.22 I'm following steps in Windows section https://github.com/AlbertoRuiz/hmatrix/blob/master/INSTALL.md I downloaded hmatrix sources and gsl-lapack-windows.zip. When I try to install hmatrix with: D:\Projects\workspace\hmatrix-0.16.1.5>cabal install --extra-include-dirs=D:\Projects\workspace\gsl-lapack-windows\ --extra-lib-dirs=d:\Projects\workspace\gsl-lapack-windows\ I got this message that I'm still cannot solve: Configuring hmatrix-0.16.1.5... Failed to install

lapack library for scip optimization

由 佐手、 提交于 2019-12-06 22:07:53
I have a quadratic optimization problem with linear constraints that I want to solve using SCIP . The optimization matrix that I want to be minimized is positive semi-definite (it is the variance of certain variables, to be precise). I have the problem in a file in CPLEX LP format and when I optimize in SCIP , I get the message Quadratic constraint handler does not have LAPACK for eigenvalue computation. Will assume that matrices (with size > 2x2) are indefinite. So SCIP starts optimization assuming that the matrix is indefinite and takes a large amount of time. I have installed LAPACK and

Efficient numpy / lapack routine for product of inverse and sparse matrix?

由 馋奶兔 提交于 2019-12-06 15:02:47
问题 I have a matrix B that is square and dense, and a matrix A that is rectangular and sparse. Is there a way to efficiently compute the product B^-1 * A ? So far, I use (in numpy) tmp = B.inv() return tmp * A which, I believe, makes us of A's sparsity. I was thinking about using the sparse method numpy.sparse.linalg.spsolve , but this requires B, and not A, to be sparse. Is there another way to speed things up? 回答1: Since the matrix to be inverted is dense, spsolve is not the tool you want. In

Armadillo + Matlab Mex segfault

由 强颜欢笑 提交于 2019-12-06 11:50:21
I fiddled with this the whole day, so I thought I might make everyone benefit from my experience, please see my answer below. I first had a problem with running a compiled Mex file within Matlab, because Matlab complained that it couldn't open the shared library libarmadillo . I solved this using the environment variables LD_LIBRARY_PATH and LD_RUN_PATH ( DYLD_LIBRARY_PATH and LYLD_RUN_PATH in osx). The problem remained however, that a simple test file would segfault at runtime even though the exact same code would compile and run fine outside Matlab (not Mex'd). The segfault seems to be
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