lapack

问题 I installed liblapack-dev and its dependencies using Synaptic, and I included <lapack.h> in my code. If I try to compile my program like this... mpicc program.c -llapack -o output ...I get the following error: program.c:4:20: fatal error: lapack.h: No such file or directory compilation terminated. How can I fix this? I've already spent hours googling for a solution but nothing helped. I'm using Linux Mint, but I tried the same thing on the latest version of Ubuntu and it still wouldn't work.

问题 I installed liblapack-dev and its dependencies using Synaptic, and I included <lapack.h> in my code. If I try to compile my program like this... mpicc program.c -llapack -o output ...I get the following error: program.c:4:20: fatal error: lapack.h: No such file or directory compilation terminated. How can I fix this? I've already spent hours googling for a solution but nothing helped. I'm using Linux Mint, but I tried the same thing on the latest version of Ubuntu and it still wouldn't work.

问题 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

问题 I am using py2exe to convert a script which uses numpy and am getting a very large resulting folder, and it seems a lot of the large files are from parts of the numpy package that I'm not using, such as numpy.linalg . To reduce the size of folder that is created, I have been led to believe I should have numpy compiled without Intel MKL/BLAS/ATLAS/LAPACK. How would I make this change? EDIT In C:\Python27\Lib\site-packages\numpy\linalg I found the following files: _umath_linalg.pyd (34MB) and

问题 I wanted to install BLAS, CBLAS, LAPACK and OpenBLAS libraries from source using available packages you can download here openblas and lapack, blas/cblas. Firstly I removed my system blas/cblas and lapack libraries, but unfortunately atlas library couldn't be uninstalled (I can either have both blas and lapack or atlas - can't remove them all). I didn't bother and started compiling downloaded libraries cause I thought that after installation I would be able to remove atlas. Building process

问题 DGEQRF and SGEQRF from LAPACK return the Q part of the QR factorization in a packed format. Unpacking it seems to require O(k^3) steps (k low-rank products), and doesn't seem to be very straightforward. Plus, the numerical stability of doing k sequential multiplications is unclear to me. Does LAPACK include a subroutine for unpacking Q, and if not, how should I do it? 回答1: Yes, LAPACK indeed offers a routine to retrieve Q from the elementary reflectors (i.e. the part of data returned by

问题 For reasons I described earlier, I need to use LAPACKs dgesvd and zgesvd methods in Python instead of the ones wrapped in numpy. Someone pointed out, that I could use f2py, to create my own python package. The trouble is, that, dgesdd in lapack calls a bunch of other methods like dbdsqr, dgelqf and also some BLAS routines, and I don't know, how I should proceed about that. Can anyone point out, what would be the propper way of creating a dgesvd python module without having to recompile the

问题 I need to use a Fortran routine called dgebal (documentation here) in my Rcpparmadillo code. I have included the following headers: # include <RcppArmadillo.h> # include <math.h> However, when I try to compile my code using sourceCpp() I get the following error: error: 'dgebal_' was not declared in this scope If I further include <R_ext/Lapack.h> and <R_ext/BLAS.h> , the code compiles without error and runs fine. However the compiler throws a bunch of warnings like this: C:/PROGRA~1/R/R-32~1

问题 I want to tridiagonalize a real symmetric matrix using Fortran and LAPACK. LAPACK basically provides two routines, one operating on the full matrix, the other on the matrix in packed storage. While the latter surely uses less memory, I was wondering if anything can be said about the speed difference? 回答1: It's an empirical question, of course: but in general, nothing comes for free, and less memory/more runtime is a pretty common tradeoff. In this case, the indexing for the data is more

问题 I am using Numeric Library Bindings for Boost UBlas to solve a simple linear system. The following works fine, except it is limited to handling matrices A(m x m) for relatively small 'm'. In practice I have a much larger matrix with dimension m= 10^6 (up to 10^7). Is there existing C++ approach for solving Ax=b that uses memory efficiently. #include<boost/numeric/ublas/matrix.hpp> #include<boost/numeric/ublas/io.hpp> #include<boost/numeric/bindings/traits/ublas_matrix.hpp> #include<boost