lapack | 易学教程

Symmetric Matrix Inversion in C using CBLAS/LAPACK

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问题 I am writing an algorithm in C that requires Matrix and Vector multiplications. I have a matrix Q (W x W) which is created by multiplying the transpose of a vector J (1 x W) with itself and adding Identity matrix I , scaled using scalar a . Q = [(J^T) * J + aI]. I then have to multiply the inverse of Q with vector G to get vector M . M = (Q^(-1)) * G. I am using cblas and clapack to develop my algorithm. When matrix Q is populated using random numbers (type float) and inverted using the

Matrix-vector product with dgemm/dgemv

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问题 Using Lapack with C++ is giving me a small headache. I found the functions defined for fortran a bit eccentric, so I tried to make a few functions on C++ to make it easier for me to read what's going on. Anyway, I'm not getting th matrix-vector product working as I wish. Here is a small sample of the program. smallmatlib.cpp: #include <cstdio> #include <stdlib.h> extern "C"{ // product C= alphaA.B + betaC void dgemm_(char* TRANSA, char* TRANSB, const int* M, const int* N, const int* K, double

Wrapping a LAPACKE function using Cython

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问题 I'm trying to wrap the LAPACK function dgtsv (a solver for tridiagonal systems of equations) using Cython. I came across this previous answer, but since dgtsv is not one of the LAPACK functions that are wrapped in scipy.linalg I don't think I can use this particular approach. Instead I've been trying to follow this example. Here's the contents of my lapacke.pxd file: ctypedef int lapack_int cdef extern from "lapacke.h" nogil: int LAPACK_ROW_MAJOR int LAPACK_COL_MAJOR lapack_int LAPACKE_dgtsv

Using alternative LAPACK driver in numpy's svd method?

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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 in general seems to be more robust. 2. Matlab uses an iteration limit of 75 in the routine. (They

Configuring LAPACK in Eclipse-Photran for fortran compiler on Windows

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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: Windows 7 Cygwin installation (for GNU fortran), added to the windows PATH lapack and liblapack-devel

权威的C ++书籍指南和清单

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这篇文章是社区维基。编辑现有答案以改善此职位。它当前不接受新的答案。这个问题试图收集每年出版的数十本糟糕的C ++书籍中的珍珠。与许多其他编程语言不同，这些语言通常是在Internet上的教程中经常使用的，而无需学习一本精心编写的C ++书籍，很少有人能够快速使用C ++。这样做太大，太复杂。实际上，它是如此之大和复杂，以至于有很多非常糟糕的C ++书籍。我们不是在谈论不良的风格，而是诸如炫耀明显的事实错误和推广极差的不良编程风格之类的东西。请编辑接受的答案，以提供优质的书籍和大致的技能水平- 最好是在C ++聊天室中讨论添加内容之后。（如果常规员工不同意建议，他们可能会毫不留情地撤消您的工作。）为您亲自阅读/受益的每本书添加简短的简介/说明。随时讨论质量，标题等。符合条件的书籍将添加到列表中。具有C和C ++用户协会（ACCU）的评论的书籍具有该评论的链接。 *注意：常见问题解答和其他资源可以在 C ++标签信息和 c ++-faq下找到。 #1楼初学者入门，没有以前的编程经验 C ++ Primer *（Stanley Lippman，JoséeLajoie和Barbara E. Moo）（针对C ++ 11更新）以1k页的篇幅对C ++ 进行了非常全面的介绍，它以一种易于访问的格式涵盖了该语言的几乎所有内容

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

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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\include\ and C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\lib\ , but it doesn't seem to do anything

lapacke or clapack if programming in c/c++ in linux

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问题 My purpose of using LAPACK is to calculate the cholesky of a matrix. I am programming in C/C++ in Fedora, but I am confused over which lapack to install - LAPACK with lapacke or clapack? 回答1: The basic difference between the two is the need for a Fortran compiler. CLAPACK is basically just the reference NETLIB LAPACK routines passed through the old f2c converter, allowing the library to be compiled with a C compiler. LAPACKE is an attempt (started by Intel IIRC) to define a formal C language

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

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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/recommenderlabrats/libs/recommenderlabrats.so: undefined symbol: dgels_ Error: loading failed Execution halted ERROR:

memory leak in dgemm_

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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 is: ==78182== 18 bytes in 1 blocks are definitely lost in loss record 2 of 30 ==78182== at 0xB936: