What you need is an iterative eigenvalue solve algorithm. LAPACK uses a direct eigensolver and having an estimation of eigenvectors is of no use. There is a QR iterative refinement in its routines. However It requires Hessenberg matrices. I do not think you could use these routines. You could use...

fortran,static-libraries,macports,lapack,fortran95

By default, macports installs everything under the prefix /opt/local, so any libraries will be located in /opt/local/lib. To link against minpack provided my macports, you should include this path with a -L flag, which specifies a library search path. gfortran -o test testprogram.f95 -L/opt/local/lib -lminpack ...

You need to install the *-devel versions of those packages. E.g., with a virtual Fedora 17 system I had lying around: $ cat main.f90 program main print *, 'hello world' end program main $ gfortran -L. main.f90 -llapack -lblas -o main /usr/bin/ld: cannot find -llapack /usr/bin/ld: cannot find -lblas collect2:...

python,numpy,install,lapack,blas

Alright, here is the whole story. First, the initial setup was slow because BLAS is a reference implementation which is not designed to be fast. I repeat, as of today, the package blas in the ArchLinux Extra repository is the reference implementation. For details, see the Presentation section here. Secondly,...

python,numpy,distutils,lapack,f2py

I think you just need ctypes, there is a complete example on calling a lapack function on this page: http://www.sagemath.org/doc/numerical_sage/ctypes.html You get your function like this: import ctypes from ctypes.util import find_library lapack = ctypes.cdll.LoadLibrary(find_library("lapack")) dgtsv = lapack.dgtsv_ ...

c,linear-algebra,lapack,lapacke

When it comes to documentation for BLAS and/or LAPACK, Intel is probably the most comprehensive out there. You can look up the docs for ?ptsv, which explains what each parameter is for. (Hint: when searching for a BLAS or LAPACK in Google, be sure to drop the s/d/c/z prefix.) Here's...

The work and lwork should be arrays (double precision and integer) and they must be properly sized according to the manual. Otherwise the LAPACK will use some part of memory which it shouldn't use and everything will blow up. There are probably other arguments wrong as well. See the source...

optimization,docker,lapack,blas

No, I think you're pretty much right. The image you link to is an automated build, so OpenBlas will be getting compiled using the kernel from the Docker build server. I did notice the build script sets the following flags when building OpenBlas: DYNAMIC_ARCH=1 NO_AFFINITY=1 NUM_THREADS=32 Which presumably makes the...

As you already noticed the A matrix is overwritten with P*L*U decomposition. If the size of the matrix is not so big, you can copy the contents of A matrix and use the copy for the decomposition. CALL CCOPY(N*N, A, 1, A_NEW, 1) If the matrix size is so big...

You want to have "C" linkage when mixing C++ and C. See the explanation there In C++ source, what is the effect of extern "C"? Indeed modifying the lines /* DSYEV prototype */ extern void dsyev( char* jobz, char* uplo, int* n, double* a, int* lda, double* w, double* work,...

I assume, that if you are new to C++, you are also new to C and Fortran. In that case I would definitely suggest to you, not to start with Blas/Lapack, at least not without a nice C++-wrapper. My suggestion would be to have a look at Eigen which offers...

So I figured out my problem(s) -- there were several,-- and I thought I should answer my own question so that others might benefit. First off, my LAPACK installation was incorrect. I had downloaded the 64-bit version instead of the 32-bit version. Even though it's 2015, somehow I'm stuck using...

As far as I can tell, there could be a number of problems: Your integers with INTEGER*8 might be too long, maybe INTEGER*4 or simply INTEGER would be better You call SGESV on double arguments instead of DGESV Your LDA argument is missing, so your code should perhaps look like...

swift,matrix,lapack,eigenvector,accelerate-framework

The problem’s with your lwork variable. This is supposed to be the size of the workspace you supply, with -1 meaning you’re performing a “workspace query”: LWORK (input) INTEGER The dimension of the array WORK. LWORK >= max(1,3*N), and if JOBVL = 'V' or JOBVR = 'V', LWORK >= 4*N....

After some more research and overthinking things, I found the culprit: Using LAPACK_ROW_MAJOR switches the meaning of the ld* leading dimension parameters. While the leading dimension of a normal Fortran array is the numbers of rows, switching to ROW_MAJOR switches its meaning to the number of columns. So the correct...

multithreading,optimization,lapack,blas,eigenvalue

You are correct expecting multi-threaded behavior mainly from BLAS and not LAPACK routines. The size of the matrices is big enough to utilize multi-threaded environment. I am not sure about the extend of BLAS usage in ZGGEV routine, but it should be more than a spike. Regarding your specific questions....

You are looking for is BLAS_DIR and LAPACK_DIR variable. set(BLAS_DIR /path/to/blas) find_package(BLAS REQUIRED) set(LAPACK_DIR /path/to/lapack) find_package(LAPACK REQUIRED) ...

c++,lapack,blas,absolute-value,argmax

BLAS was designed to provide low-level routines necessary to implement common linear-algebra operations (it is the "Basic Linear Algebra Subprograms", after all). To name just one of many uses, getting the largest-magnitude element of a vector is necessary for pivot selection in LU factorization, which is one of the most...

c,lapack,eigenvalue,eigenvector

You can not port directly from zgeev to dgeev. The zgeev gets a complex matrix and computes complex eigenvalues. While dgeev gets a real matrix and computes complex eigenvalues. In order to be consistent LAPACK uses WR and WI which is used for the real and imaginary part of each...

You are right, the problem is in incx value - it should be 1, take a look at reference. INCX is INTEGER On entry, INCX specifies the increment for the elements of X. INCX must not be zero. So this value should be used when values of vector x is...

c,dynamic,static,malloc,lapack

a is not a 2d array, it is an array of pointers to separate 1d arrays. Passing *a to LAPACKE_dgels only gives it a pointer to the first row. It will have no way to know where all of the other rows were allocated since they were allocated independently. It...

You could just use numpy.linalg.cholesky. Also if all of one column or all of one row are zeros, the matrix will be singular, have at least on eigenvalue that will be zero and therefore, not be positive definite. Since Cholesky is only defined for matrices that are "Hermitian (symmetric if...

The LAPACK routines are written in FORTRAN and data are stored column major.. You are solving the transposed A matrix system A^T x = b. Try using A = {1, 1, -3, -1}. You are correct, INF means "infinity" and NaN means "Not A Number". The LU algorithm always uses...

python,numpy,linear-algebra,cython,lapack

OK, I figured it out eventually - it seems I've misunderstood what row- and column-major refer to in this case. Since C-contiguous arrays follow row-major order, I assumed that I ought to specify LAPACK_ROW_MAJOR as the first argument to LAPACKE_dgtsv. In fact, if I change info = LAPACKE_dgtsv(LAPACK_ROW_MAJOR, ...) to...

Your assignsigx and assignsigz functions are allocating space for their return values based on undefined values of the row and col components of the respective return value. You probably want to pass in the row and col sizes you want to use, just as you do in the assign0 and...

lapack,hpc,scientific-computing,intel-mkl

The function LAPACKE_dptsv() corresponds to the lapack function dptsv(), which does not feature the switch between LAPACK_ROW_MAJOR and LAPACK_COL_MAJOR. dptsv() is implemented for column-major ordering, corresponding to matrices in Fortran, while most of C matrices are row-major. So LAPACKE_dptsv(LAPACK_ROW_MAJOR,...) performs the following steps : transpose the right-end side b call...

The lapack function ilaver() is made for you ! Its prototype is self-explaining : subroutine ilaver ( integer VERS_MAJOR, integer VERS_MINOR, integer VERS_PATCH ) Here are two programs demonstrating how to use it : in a fortran program, complied by gcc main.f90 -o main -llapack PROGRAM VER IMPLICIT NONE INTEGER...

multithreading,fortran,lapack,blas

The LAPACK library is expected to be thread safe. It does not support multiple threads, so it does not use (all) your systems cores. Actually there is a specific declaration that all LAPACK subroutines are thread-safe since v3.3. On the other hand LAPACK is designed to use extensively BLAS library...

parallel-processing,fortran,lapack,blas,scalapack

Scalapack library uses naming conversion to declare single or double precision function. This declaration is done by the second letter of scalapack function The "PD*" function means a double precision function, while "PS*" means single. So, you should change DBLESZ = 8 to DBLESZ = 4 All DOUBLE PRECISION to...

javascript,matlab,linear-algebra,lapack

I ended up finding success using the Eigen library, combined with Emscripten. Right now, my test code is hard-coded to 5x5 matrices, but that's just a matter of template arguments. I'm passing data to and from the function by using row major 1D arrays. The code looks something like: #include...

c++,matlab,linear-algebra,mex,lapack

I've tried to reproduce with an example on my end, but I'm not seeing any errors. In fact the result is identical to MATLAB's. mex_chol.cpp #include "mex.h" #include "lapack.h" void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) { // verify arguments if (nrhs != 1 || nlhs >...

Check your function names carefully. The linker is complaining about an undefined reference to function dgeev(). The working code is calling a different function, named dgeev_(). Compiling with the option -D as follows: [email protected]:~/Desktop/tests$ gcc -Ddgeev=dgeev_ -o lapack1 lapack1.c -L/usr/local/lib -llapack -lblas && ./lapack1 Will indeed work....

After some more messing around, I figured out my (rather elementary) mistake. You need to specify the -lmwlapack option (and/or -lmwblas, as appropriate) to the linker: mex -largeArrayDims -lmwlapack matrixDivide.c It even says so on the MathWorks page I was trying to follow along on. RTFM!...