lapack

问题 I am getting an error when i test CBLAS in CLAPACK. When i run the test code ./xblat2d < dblat2.in I get the error output: "TransA must be 111, 112 or 113, but is set to -1Parameter 2 to routine cblas_dgemv was incorrect" Does anyone know what kind of problem this indicates? Details I am trying not to use reference CBLAS, and use ATLAS CBLAS instead. So, i compiled wrapper library libcblaswr.a and changed the line in make.inc to BLASLIB = ../../libcblaswr.a -L/usr/local/atlas/lib -lcblas -L

问题 This is a continuation from a previous question: Rfast hd.eigen() returns NAs but base eigen() does not I have been having a problem with .Internal(La_rs((x)) returning different results on different machines. I suspect it may have something to do with number formatting, because on the same machine, if I save as a CSV and re-open, I don't get negatives anymore: On Clear Linux install: > load("input_to_La_rs.Rdata") > r <- .Internal(La_rs(as.matrix(x), only.values = FALSE)) > sum(r$values < 0)

问题 I have two GCC compilers installed on a Linux (CentOS) machine. The old version of GCC (4.4.7) is in the default folder (came with CentOS) and the newer one that I intend to use is in /usr/local/gcc/4.9.3/. My code utilizes MPI and LAPACK/LAPACKE/BLAS libraries and with the old GCC I used to compile source (for example “main.cpp”) like this: mpiCC main.cpp -o main -L/home/USER1/lapack-3.6.1 -llapacke -llapack -lblas -lm –Wall This still invokes the old GCC 4.4.7. What should I modify so the

问题 The situation is simple, but strange. When i compile my program without the LinearAlgebra.o source (which requires linking to LAPACK), C++ exceptions are caught and handled. When I do not include that compilation unit but still link to the libraries ( -llapack -lblas ), exceptions are caught and handled. But once I get it in there (the code from it runs just fine), C++ exceptions are no longer handled correctly, and I get Windows crash handler "Program has stopped responding report back to HQ

问题 i have been trying for hours and it drives me crazy. The last error I get is : demo_cblas.c:(.text+0x83): undefined reference to `clapack_sgetrf' demo_cblas.c:(.text+0xa3): undefined reference to `clapack_sgetri' I am compiling the code using /usr/bin/gcc -o demo_cblas demo_cblas.c -L /usr/lib64 -l :libgfortran.so.3 -L /usr/lib64 \ -llapack -L /usr/lib64 -lblas I try with and without libgfortran, with different compilers gcc-33, gcc-47, gcc-48. The test code is not from me but comes from this

问题 I'm trying to install scipy module in python using pip...it is showing following error.. Command "c:\users\sony\appdata\local\programs\python\python35-32\python.exe -u -c "import setuptools, tokenize;__file__='C:\\Users\\sony\\AppData\\Local\\Temp\\pip-build- vmyvqdgv\\scipy\\setup.py';exec(compile(getattr(tokenize, 'open', open) (__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install -- record C:\Users\sony\AppData\Local\Temp\pip-mf_0tla_-record\install- record.txt --single

问题 Solve Ax = b . Real double. A is overdetermined Mx2 with M >> 2. b is Mx1. I've run a ton of data against mldivide , and the results are excellent. I wrote a mex routine with MKL LAPACKE_dgels and it's nowhere near as good. The results have a ton of noise and the underlying signal is barely there. I checked the routine against the MKL example results first. I've searched through the mldivide doc (flowchart) and the SO questions. All I found is Matlab uses QR factorization for overdetermined

问题 See further update below I am observing a quiet high system CPU usage when running my Fortran code. The "user CPU usage" is taking about one core (system is an Intel i7 with 4 cores/ 8 threads, running Linux) whilst system CPU is eating up about 2 cores (hence overall CPU usage about 75%). Can anyone explain to me where this is coming from and if this is "normal" behaviour? I compile the code with gfortran (optimization turned off -O0, though that part doesn't seem to matter) and link against

问题 I define the function that calculates the determinant of a matrix here. But sometimes I get the wrong sign. I modeled my function from this answer. from scipy.linalg.cython_lapack cimport dgetrf cpdef double det_c(double[:, ::1] A, double[:, ::1] work, double[::1] ipiv): '''obtain determinant of float type square matrix A Notes ----- As is, this function is not yet computing the sign of the determinant correctly, help! Parameters ---------- A : memoryview (numpy array) n x n array to compute

问题 I'm on an Ubuntu 14.04. I would have expected doing: sudo apt-get install python-numpy would've worked but it isn't the case... The way I tried to check is by doing locate blas and found nothing that seemed relevant. I would like a solution that doesn't involve me compiling from source. The ideal solution is something that uses the ubuntu repos. 回答1: In Ubuntu 14.04 and later, blas and lapack are installed as part of python-scipy and python3-scipy, so you need to install python-scipy. Open