On entry to DGEEV parameter number 9 had an illegal value - c

I am trying for the first time to use LAPACK from C to diagonalize a matrix and I am stuck.
I have been trying to modify this example http://rcabreral.blogspot.co.uk/2010/05/eigenvalues-clapack.html from zgeev to dgeev. I have looked at the DGEEV input parameters, http://www.netlib.org/lapack/explore-html/d9/d28/dgeev_8f.html but it seems I don't understand the well enough.
Hence, the code below produces:
**** On entry to DGEEV parameter number 9 had an illegal value**
EDIT: The error occurs in the call of dgeev spanning lines 48 to (including) 53.
EDIT: Note that the arguments differ from the specifications here
http://www.netlib.org/lapack/explore-html/d9/d28/dgeev_8f.html
in that they have been translated to pointers. That is necessary when using these Fortran routines in C, as explained here:
http://www.physics.orst.edu/~rubin/nacphy/lapack/cprogp.html
#include <stdio.h>
#include <math.h>
#include <complex.h>
#include <stdlib.h>
//.........................................................................
void dgeTranspose( double *Transposed, double *M ,int n) {
int i,j;
for(i=0;i<n;i++)
for(j=0;j<n;j++)
Transposed[i+n*j] = M[i*n+j];
}
//.........................................................................
// MatrixComplexEigensystem: computes the eigenvectors and eigenValues of input matrix A
// The eigenvectors are stored in columns
//.........................................................................
void MatrixComplexEigensystem( double *eigenvectorsVR, double *eigenvaluesW, double *A, int N){
int i;
double *AT = (double *) malloc( N*N*sizeof(double ) );
dgeTranspose( AT, A , N);
char JOBVL ='N'; // Compute Right eigenvectors
char JOBVR ='V'; // Do not compute Left eigenvectors
double VL[1];
int LDVL = 1;
int LDVR = N;
int LWORK = 4*N;
double *WORK = (double *)malloc( LWORK*sizeof(double));
double *RWORK = (double *)malloc( 2*N*sizeof(double));
int INFO;
dgeev_( &JOBVL, &JOBVR, &N, AT , &N , eigenvaluesW ,
VL, &LDVL,
eigenvectorsVR, &LDVR,
WORK,
&LWORK, RWORK, &INFO );
dgeTranspose( AT, eigenvectorsVR , N);
for(i=0;i<N*N;i++) eigenvectorsVR[i]=AT[i];
free(WORK);
free(RWORK);
free(AT);
}
int main(){
int i,j;
const int N = 3;
double A[] = { 1.+I , 2. , 3 , 4. , 5.+I , 6. , 7., 8., 9. + I};
double eigenVectors[N*N];
double eigenValues[N];
MatrixComplexEigensystem( eigenVectors, eigenValues, A, N);
printf("\nEigenvectors\n");
for(i=0;i<N;i++){
for(j=0;j<N;j++) printf("%e", eigenVectors[i*N + j]);
printf("\n");
}
printf("\nEigenvalues \n");
for(i=0;i<N;i++) printf("%e", eigenValues[i] );
printf("\n------------------------------------------------------------\n");
return 0;
}

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 eigenvalue.
So note that dgeev definition is
void dgeev_(char* JOBVL, char* JOBVR, int* N, double* A, int* LDA, double* WR, double* WI, double* VL, int* LDVL, double* VR, int* LDVR, double* WORK, int* LWORK, int* INFO);
My suggestion for your example is to remove:
#include <complex.h>
remove I's from matrix of doubles:
double A[] = { 1. , 2. , 3 , 4. , 5. , 6. , 7., 8., 9.};
then double the size of eigenvalues vector:
double eigenValues[2*N];
and call dgeev using WR and WI:
double *eigenvaluesWR = eigenvaluesW;
double *eigenvaluesWI = eigenvaluesW+N;
dgeev_(&JOBVL, &JOBVR, &N, AT, &N,
eigenvaluesWR, eigenvaluesWI,
VL, &LDVL,
eigenvectorsVR, &LDVR,
WORK, &LWORK, &INFO);

Related

How do I access the dynamically allocated 2D array of type fftw_complex in FFTW after a 2D r2c transformation?

#include<stdio.h>
#include<fftw3.h>
#include<math.h>
#include <stdlib.h>
int main(){
int N=2000;
int i,j;
FILE *filepointer;
filepointer=fopen("2DDFT_spacetime.plt","w");
//double in[N][N];
double *in;
fftw_complex *out;
fftw_plan p;
double fx=13.0;
double fz=9.0;
double x[N];
double xstart=0.0;
double xend=5.0/fx;
double z[N];
double zstart=0.0;
double zend=5.0/fz;
double dx=(xend-xstart)/(N-1);
double dz=(zend-zstart)/(N-1);
x[0]=xstart;
z[0]=zstart;
in = (double*) malloc(sizeof(double) * N * N); //allocates input array
out = fftw_alloc_complex(N*((int)floor(N/2)+1)); //wrapper function ;allocates output array
p = fftw_plan_dft_r2c_2d(N,N, in, out, FFTW_MEASURE);
for(i=1;i<N;i++) {
x[i]=x[i-1]+dx;
}
for(i=1;i<N;i++) {
z[i]=z[i-1]+dz;
}
for(i=0;i<N;i++) {
for(j=0;j<N;j++) {
in[i*N+j]=cos(2*M_PI*fx*x[i]+2*M_PI*fz*z[j]);
}
}
fftw_execute(p);
fprintf(filepointer,"TITLE =\"FFTW\"\nVARIABLES=\"Wavenumber-x\"\n\"Wavenumber-z\"\n\"Amplitude\"\nZONE T=\"Amplitude\"\n I=%d, J=%d, K=1, ZONETYPE=Ordered\n DATAPACKING=POINT\n DT=(SINGLE SINGLE SINGLE)\n",N,(int)floor(N/2)+1);
for(j=0;j<(int)floor(N/2)+1;j++) {
for(i=0;i<N;i++) {
fprintf(filepointer," %.9E %.9E %.9E\n",i/(xend-xstart),j/(zend-zstart),sqrt(pow(out[i*((int)floor(N/2)+1)+j][0],2)+pow(out[i*((int)floor(N/2)+1)+j][1],2)));
}
}
fftw_destroy_plan(p);
free(in);
fftw_free(out);
fclose(filepointer);
return(1);
}
I begin by allocating memory for a NxN double array and a NxN fftw_complex array, which is defined as typedef double fftw_complex[2] in the FFTW library. I assign real numbers to the array of doubles, do the real to complex FFT, and get the output in the array of fftw_complex.
Should I access the real and imaginary parts of the output complex number as out[i*((int)floor(N/2)+1)+j][0] and out[i*((int)floor(N/2)+1)+j][1] respectively?
The most beautiful course of action is to include the standard native complex type #include <complex.h> before <fftw3.h> as signaled in the documentation of FFTW.
In particular, if you #include before , then fftw_complex is defined to be the native complex type and you can manipulate it with ordinary arithmetic (e.g. x = y * (3+4*I), where x and y are fftw_complex and I is the standard symbol for the imaginary unit);
The following example is compiled using gcc main.c -o main -Wall -lfftw3 -lm
#include<stdlib.h>
#include<math.h>
#include<complex.h>
#include<fftw3.h>
int main(void){
fftw_plan p;
unsigned long int N = 10;
fftw_complex *in=fftw_malloc(N*sizeof(fftw_complex));
if(in==NULL){fprintf(stderr,"malloc failed\n");exit(1);}
fftw_complex *out=fftw_malloc(N*sizeof(fftw_complex));
if(out==NULL){fprintf(stderr,"malloc failed\n");exit(1);}
printf("sizeof fftw complex %ld\n",sizeof(fftw_complex));
p=fftw_plan_dft_1d(N, in, out, FFTW_FORWARD, FFTW_ESTIMATE);
if (p==NULL){fprintf(stderr,"plan creation failed\n");exit(1);}
unsigned int i;
for(i=0;i<N;i++){
in[i]=30.+12.*sin(2*3.1415926535*i/((double)N));
}
fftw_execute(p);
for (i = 0; i < N; i++){
printf("result: %d || %g %gI |\n", i, creal(out[i]), cimag(out[i]));
}
fftw_destroy_plan(p);
fftw_free(in);
fftw_free(out);
return(0);
}
It shows how to retreive the imaginary and real part of a complex.
Now any function operating on complex number can be used, as listed in https://en.cppreference.com/w/c/numeric/complex .

LAPACKE matrix inversion segmentation fault C

I am trying to write a code using lapacke libraries to invert a complex matrix in C. However I am stuck with a segmentation fault which seems to depend on the size N of the matrix. What's more is that the size for which the segmentation fault happens varies each time I compile the program or I touch anything. This makes me think that somewhere the code is trying to access badly allocated or forbidden memory. Unfortunately, I dont' understand how this happens since it seems to be related with the LAPACKE functions themeselves. In fact, when the function /*MatrixComplexInv(invA,A,N);*/ ( in which the LAPACKE functions are called for the inversion) is commented the segmentation fault doesn't happen.
Below there is the working code that can be compiled and run on its own.
#include <stdio.h>
#include <lapacke.h>
#include <complex.h>
#include <stdlib.h>
#include <math.h>
void Ctranspose( double complex *, double complex * ,int );
void MatrixComplexInv(double complex *, double complex *, int );
int main(int argc, const char * * argv) {
int i,j,k,N = 4;/*if N> bigger than a small number 4,6,7.. it gives segmentation fault*/
double complex *A = calloc(N*N,sizeof(double complex)),
*b = calloc(N*N,sizeof(double complex)),
*Ap =calloc(N*N,sizeof(double complex));
double complex *invA =calloc(N*N,sizeof(double complex));
for(i=0;i<N;i++){
for(j=0;j<N;j++){
A[i*N+j] = 1+sin(i*j)*i+I*j;
Ap[i*N+j] = 1+sin(i*j)*i+I*j;
}
}
/*Segmentation fault in this function, due to
*
LAPACKE_zgetrf(LAPACK_ROW_MAJOR, n, n, tempA , n,&n);
LAPACKE_zgetri(LAPACK_ROW_MAJOR, n, tempA , n, &n );
*
* both.
*/
MatrixComplexInv(invA,A,N);
for(i=0;i<N;i++){
for(j=0;j<N;j++){
for(k = 0;k<N;k++){
b[i*N+j]+=invA[i*N + k]*Ap[k*N + j];
}
printf("(%lf,%lf)\t", creal(b[i*N + j]),cimag(b[i*N + j]));/*tests that the result produces the inverse matrix A^{-1}A = 1*/
}
printf("\n");
}
return 0;
}
void Ctranspose( double complex *Transposed, double complex *M ,int n)
{
int i,j;
for(i=0;i<n;i++)
for(j=0;j<n;j++) Transposed[i+n*j] = M[i*n+j];
}
void MatrixComplexInv(double complex *invA, double complex *A, int n)
{
double complex *tempA = (double complex*) malloc( n*n*sizeof(double complex) );
Ctranspose(tempA,A,n);
/*SEGMENTATION HAPPEN IN THESE TWO FUNCTIONS*/
LAPACKE_zgetrf(LAPACK_ROW_MAJOR, n, n, tempA , n,&n);
LAPACKE_zgetri(LAPACK_ROW_MAJOR, n, tempA , n, &n );
Ctranspose(invA,tempA,n);
free(tempA);
}
In LAPACKE_zgetrf(LAPACK_ROW_MAJOR, n, n, tempA , n,&n);, the last argument of LAPACKE_zgetrf points to n, a single integer. On the contrary, the argument ipiv should be a pointer to an array of integer of dimension max(m,n), to store pivot indices This could explain a segmentation fault.
The ipiv computed by LAPACKE_zgetrf() must also be provided to LAPACKE_zgetri() as input, to get the correct inverse matrix.

How do I parallelize this triple loop in an efficient way?

I'm trying to parallelize a function which takes as input three arrays (x, y, and prb) and one scalar, and outputs three arrays (P1, Pt1, and Px).
The original c code is here (the outlier and E are inconsequential):
#include <stdio.h>
#include <math.h>
#include <stdlib.h>
#define max(A, B) ((A) > (B) ? (A) : (B))
#define min(A, B) ((A) < (B) ? (A) : (B))
void cpd_comp(
double* x,
double* y,
double* prb,
double* sigma2,
double* outlier,
double* P1,
double* Pt1,
double* Px,
double* E,
int N,
int M,
int D
)
{
int n, m, d;
double ksig, diff, razn, outlier_tmp, sp;
double *P, *temp_x;
P = (double*) calloc(M, sizeof(double));
temp_x = (double*) calloc(D, sizeof(double));
ksig = -2.0 * *sigma2;
for (n=0; n < N; n++) {
sp=0;
for (m=0; m < M; m++) {
razn=0;
for (d=0; d < D; d++) {
diff=*(x+n+d*N)-*(y+m+d*M); diff=diff*diff;
razn+=diff;
}
*(P+m)=exp(razn/ksig) ;
sp+=*(P+m);
}
*(Pt1+n)=*(prb+n);
for (d=0; d < D; d++) {
*(temp_x+d)=*(x+n+d*N)/ sp;
}
for (m=0; m < M; m++) {
*(P1+m)+=((*(P+m)/ sp) **(prb+n));
for (d=0; d < D; d++) {
*(Px+m+d*M)+= (*(temp_x+d)**(P+m)**(prb+n));
}
}
*E += -log(sp);
}
*E +=D*N*log(*sigma2)/2;
free((void*)P);
free((void*)temp_x);
return;
}
Here is my attempt at parallelizing it:
#include <cuda.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <thrust/device_ptr.h>
#include <thrust/reduce.h>
/*headers*/
void cpd_comp(
float * x, //Points to register [N*D]
float * y, //Points to be registered [M*D]
float * prb, //Vector of probabilities [N]
float * sigma2, //Square of sigma
float ** P1, //P1, output, [M]
float ** Pt1, //Pt1, output, [N]
float ** Px, //Px, output, [M*3]
int N, //Number of points, i.e. rows, in x
int M //Number of points, i.e. rows, in
);
__global__ void d_computeP(
float * P,
float * P1,
float * Px,
float * ProbabilityMatrix,
float * x,
float * y,
float * prb,
float ksig,
const int N,
const int M);
__global__ void d_sumP(
float * sp,
float * P1timessp,
float * Pxtimessp,
float * P1,
float * Px,
const int N,
const int M);
/*implementations*/
void cpd_comp(
float * x, //Points to register [N*D]
float * y, //Points to be registered [M*D]
float * prb, //Vector of probabilities [N]
float * sigma2, //Scalar
float ** P1, //P1, output, [M]
float ** Pt1, //Pt1, output, [N]
float ** Px, //Px, output, [M*3]
int N, //Number of points, i.e. rows, in x
int M //Number of points, i.e. rows, in y
){
//X is generatedPointPos
//Y is points
float
*P,
*P1timessp,
*Pxtimessp,
ksig = -2.0 * (*sigma2),
*h_sumofP = new float[N], //sum of P, on host
*d_sumofP; //sum of P, on device
cudaMalloc((void**)&P, sizeof(float)*M*N);
cudaMalloc((void**)&P1timessp,sizeof(float)*M*N);
cudaMalloc((void**)&Pxtimessp,sizeof(float)*M*N*3);
cudaMalloc((void**)&d_sumofP, sizeof(float)*N);
cudaMalloc((void**)P1, sizeof(float)*M);
cudaMalloc((void**)Px, sizeof(float)*M*3);
cudaMalloc((void**)Pt1, sizeof(float)*N);
d_computeP<<<dim3(N,M/1024+1),M>1024?1024:M>>>(P,P1timessp,Pxtimessp,NULL,x,y,prb,ksig,N,M);
for(int n=0; n<N; n++){
thrust::device_ptr<float>dev_ptr(P);
h_sumofP[n] = thrust::reduce(dev_ptr+M*n,dev_ptr+M*(n+1),0.0f,thrust::plus<float>());
}
cudaMemcpy(d_sumofP,h_sumofP,sizeof(float)*N,cudaMemcpyHostToDevice);
d_sumP<<<M/1024+1,M>1024?1024:M>>>(d_sumofP,P1timessp,Pxtimessp,*P1,*Px,N,M);
cudaMemcpy(*Pt1,prb,sizeof(float)*N,cudaMemcpyDeviceToDevice);
cudaFree(P);
cudaFree(P1timessp);
cudaFree(Pxtimessp);
cudaFree(d_sumofP);
delete[]h_sumofP;
}
/*kernels*/
__global__ void d_computeP(
float * P,
float * P1,
float * Px,
float * ProbabilityMatrix,
float * x,
float * y,
float * prb,
float ksig,
const int N,
const int M){
//thread configuration: <<<dim3(N,M/1024+1),1024>>>
int m = threadIdx.x+blockIdx.y*blockDim.x;
int n = blockIdx.x;
if(m>=M || n>=N) return;
float
x1 = x[3*n],
x2 = x[3*n+1],
x3 = x[3*n+2],
diff1 = x1 - y[3*m],
diff2 = x2 - y[3*m+1],
diff3 = x3 - y[3*m+2],
razn = diff1*diff1+diff2*diff2+diff3*diff3,
Pm = __expf(razn/ksig), //fast exponentiation
prbn = prb[n];
P[M*n+m] = Pm;
__syncthreads();
P1[N*m+n] = Pm*prbn;
Px[3*(N*m+n)+0] = x1*Pm*prbn;
Px[3*(N*m+n)+1] = x2*Pm*prbn;
Px[3*(N*m+n)+2] = x3*Pm*prbn;
}
__global__ void d_sumP(
float * sp,
float * P1timessp,
float * Pxtimessp,
float * P1,
float * Px,
const int N,
const int M){
//computes P1 and Px
//thread configuration: <<<M/1024+1,1024>>>
int m = threadIdx.x+blockIdx.x*blockDim.x;
if(m>=M) return;
float
P1m = 0,
Pxm1 = 0,
Pxm2 = 0,
Pxm3 = 0;
for(int n=0; n<N; n++){
float spn = 1/sp[n];
P1m += P1timessp[N*m+n]*spn;
Pxm1 += Pxtimessp[3*(N*m+n)+0]*spn;
Pxm2 += Pxtimessp[3*(N*m+n)+1]*spn;
Pxm3 += Pxtimessp[3*(N*m+n)+2]*spn;
}
P1[m] = P1m;
Px[3*m+0] = Pxm1;
Px[3*m+1] = Pxm2;
Px[3*m+2] = Pxm3;
}
However, to my horror, it runs much, much slower than the original version. How do I make it run faster? Please explain things thoroughly since I am very new to CUDA and parallel programming and have no experience in algorithms.
Do note that the c version has column-major ordering and the CUDA version has row-major. I have done several tests to make sure that the result is correct. It's just extremely slow and takes up a LOT of memory.
Any help is greatly appreciated!
EDIT: More information: N and M are on the order of a few thousand (say, 300-3000) and D is always 3. The CUDA version expects arrays to be device memory, except for variables prefixed with h_.
Before trying any CUDA-specific optimizations, profile your code to see where time is being spent.
Try and arrange your array reads/writes so that each CUDA thread uses a strided access pattern. For example, currently you have
int m = threadIdx.x+blockIdx.y*blockDim.x;
int n = blockIdx.x;
if(m>=M || n>=N) return;
diff1 = x1 - y[3*m],
diff2 = x2 - y[3*m+1],
diff3 = x3 - y[3*m+2],
So thread 1 will read from y[0],y[1],y[2] etc. Instead, rearrange your data so that thread 1 reads from y[0],y[M],y[2*M] and thread 2 reads from y[1],y[M+1],y[2*M+1] etc. You should follow this access pattern for other arrays.
Also, you may want to consider whether you can avoid the use of __syncthreads(). I don't quite follow why it's necessary in this algorithm, it might be worth removing it to see if it improves performance ( even if it produces incorrect results ).
The key to good CUDA performance is almost always to make as near to optimal memory access as possible. Your memory access pattern looks very similar to matrix multiplication. I would start with a good CUDA matrix multiplication implementation, being sure to understand why it's implemented the way it is, and then modify that to suit your needs.

C File Input/Trapezoid Rule Program

Little bit of a 2 parter. First of all im trying to do this in all c. First of all I'll go ahead and post my program
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <omp.h>
#include <string.h>
double f(double x);
void Trap(double a, double b, int n, double* integral_p);
int main(int argc, char* argv[]) {
double integral=0.0; //Integral Result
double a=6, b=10; //Left and Right Points
int n; //Number of Trapezoids (Higher=more accurate)
int degree;
if (argc != 3) {
printf("Error: Invalid Command Line arguements, format:./trapezoid N filename");
exit(0);
}
n = atoi(argv[2]);
FILE *fp = fopen( argv[1], "r" );
# pragma omp parallel
Trap(a, b, n, &integral);
printf("With n = %d trapezoids....\n", n);
printf("of the integral from %f to %f = %.15e\n",a, b, integral);
return 0;
}
double f(double x) {
double return_val;
return_val = pow(3.0*x,5)+pow(2.5*x,4)+pow(-1.5*x,3)+pow(0*x,2)+pow(1.7*x,1)+4;
return return_val;
}
void Trap(double a, double b, int n, double* integral_p) {
double h, x, my_integral;
double local_a, local_b;
int i, local_n;
int my_rank = omp_get_thread_num();
int thread_count = omp_get_num_threads();
h = (b-a)/n;
local_n = n/thread_count;
local_a = a + my_rank*local_n*h;
local_b = local_a + local_n*h;
my_integral = (f(local_a) + f(local_b))/2.0;
for (i = 1; i <= local_n-1; i++) {
x = local_a + i*h;
my_integral += f(x);
}
my_integral = my_integral*h;
# pragma omp critical
*integral_p += my_integral;
}
As you can see, it calculates trapezoidal rule given an interval.
First of all it DOES work, if you hardcode the values and the function. But I need to read from a file in the format of
5
3.0 2.5 -1.5 0.0 1.7 4.0
6 10
Which means:
It is of degree 5 (no more than 50 ever)
3.0x^5 +2.5x^4 −1.5x^3 +1.7x+4 is the polynomial (we skip ^2 since it's 0)
and the Interval is from 6 to 10
My main concern is the f(x) function which I have hardcoded. I have NO IDEA how to make it take up to 50 besides literally typing out 50 POWS and reading in the values to see what they could be.......Anyone else have any ideas perhaps?
Also what would be the best way to read in the file? fgetc? Im not really sure when it comes to reading in C input (especially since everything i read in is an INT, is there some way to convert them?)
For a large degree polynomial, would something like this work?
double f(double x, double coeff[], int nCoeff)
{
double return_val = 0.0;
int exponent = nCoeff-1;
int i;
for(i=0; i<nCoeff-1; ++i, --exponent)
{
return_val = pow(coeff[i]*x, exponent) + return_val;
}
/* add on the final constant, 4, in our example */
return return_val + coeff[nCoeff-1];
}
In your example, you would call it like:
sampleCall()
{
double coefficients[] = {3.0, 2.5, -1.5, 0, 1.7, 4};
/* This expresses 3x^5 + 2.5x^4 + (-1.5x)^3 + 0x^2 + 1.7x + 4 */
my_integral = f(x, coefficients, 6);
}
By passing an array of coefficients (the exponents are assumed), you don't have to deal with variadic arguments. The hardest part is constructing the array, and that is pretty simple.
It should go without saying, if you put the coefficients array and number-of-coefficients into global variables, then the signature of f(x) doesn't need to change:
double f(double x)
{
// access glbl_coeff and glbl_NumOfCoeffs, instead of parameters
}
For you f() function consider making it variadic (varargs is another name)
http://www.gnu.org/s/libc/manual/html_node/Variadic-Functions.html
This way you could pass the function 1 arg telling it how many "pows" you want, with each susequent argument being a double value. Is this what you are asking for with the f() function part of your question?

Computing the inverse of a matrix using lapack in C

I would like to be able to compute the inverse of a general NxN matrix in C/C++ using lapack.
My understanding is that the way to do an inversion in lapack is by using the dgetri function, however, I can't figure out what all of its arguments are supposed to be.
Here is the code I have:
void dgetri_(int* N, double* A, int* lda, int* IPIV, double* WORK, int* lwork, int* INFO);
int main(){
double M [9] = {
1,2,3,
4,5,6,
7,8,9
};
return 0;
}
How would you complete it to obtain the inverse of the 3x3 matrix M using dgetri_?
Here is the working code for computing the inverse of a matrix using lapack in C/C++:
#include <cstdio>
extern "C" {
// LU decomoposition of a general matrix
void dgetrf_(int* M, int *N, double* A, int* lda, int* IPIV, int* INFO);
// generate inverse of a matrix given its LU decomposition
void dgetri_(int* N, double* A, int* lda, int* IPIV, double* WORK, int* lwork, int* INFO);
}
void inverse(double* A, int N)
{
int *IPIV = new int[N];
int LWORK = N*N;
double *WORK = new double[LWORK];
int INFO;
dgetrf_(&N,&N,A,&N,IPIV,&INFO);
dgetri_(&N,A,&N,IPIV,WORK,&LWORK,&INFO);
delete[] IPIV;
delete[] WORK;
}
int main(){
double A [2*2] = {
1,2,
3,4
};
inverse(A, 2);
printf("%f %f\n", A[0], A[1]);
printf("%f %f\n", A[2], A[3]);
return 0;
}
First, M has to be a two-dimensional array, like double M[3][3]. Your array is, mathematically speaking, a 1x9 vector, which is not invertible.
N is a pointer to an int for the
order of the matrix - in this case,
N=3.
A is a pointer to the LU
factorization of the matrix, which
you can get by running the LAPACK
routine dgetrf.
LDA is an integer for the "leading
element" of the matrix, which lets
you pick out a subset of a bigger
matrix if you want to just invert a
little piece. If you want to invert
the whole matrix, LDA should just be
equal to N.
IPIV is the pivot indices of the
matrix, in other words, it's a list
of instructions of what rows to swap
in order to invert the matrix. IPIV
should be generated by the LAPACK
routine dgetrf.
LWORK and WORK are the "workspaces"
used by LAPACK. If you are inverting
the whole matrix, LWORK should be an
int equal to N^2, and WORK should be
a double array with LWORK elements.
INFO is just a status variable to
tell you whether the operation
completed successfully. Since not all
matrices are invertible, I would
recommend that you send this to some
sort of error-checking system. INFO=0 for successful operation, INFO=-i if the i'th argument had an incorrect input value, and INFO > 0 if the matrix is not invertible.
So, for your code, I would do something like this:
int main(){
double M[3][3] = { {1 , 2 , 3},
{4 , 5 , 6},
{7 , 8 , 9}}
double pivotArray[3]; //since our matrix has three rows
int errorHandler;
double lapackWorkspace[9];
// dgetrf(M,N,A,LDA,IPIV,INFO) means invert LDA columns of an M by N matrix
// called A, sending the pivot indices to IPIV, and spitting error
// information to INFO.
// also don't forget (like I did) that when you pass a two-dimensional array
// to a function you need to specify the number of "rows"
dgetrf_(3,3,M[3][],3,pivotArray[3],&errorHandler);
//some sort of error check
dgetri_(3,M[3][],3,pivotArray[3],9,lapackWorkspace,&errorHandler);
//another error check
}
Here is a working version of the above using OpenBlas interface to LAPACKE.
Link with openblas library (LAPACKE is already contained)
#include <stdio.h>
#include "cblas.h"
#include "lapacke.h"
// inplace inverse n x n matrix A.
// matrix A is Column Major (i.e. firts line, second line ... *not* C[][] order)
// returns:
// ret = 0 on success
// ret < 0 illegal argument value
// ret > 0 singular matrix
lapack_int matInv(double *A, unsigned n)
{
int ipiv[n+1];
lapack_int ret;
ret = LAPACKE_dgetrf(LAPACK_COL_MAJOR,
n,
n,
A,
n,
ipiv);
if (ret !=0)
return ret;
ret = LAPACKE_dgetri(LAPACK_COL_MAJOR,
n,
A,
n,
ipiv);
return ret;
}
int main()
{
double A[] = {
0.378589, 0.971711, 0.016087, 0.037668, 0.312398,
0.756377, 0.345708, 0.922947, 0.846671, 0.856103,
0.732510, 0.108942, 0.476969, 0.398254, 0.507045,
0.162608, 0.227770, 0.533074, 0.807075, 0.180335,
0.517006, 0.315992, 0.914848, 0.460825, 0.731980
};
for (int i=0; i<25; i++) {
if ((i%5) == 0) putchar('\n');
printf("%+12.8f ",A[i]);
}
putchar('\n');
matInv(A,5);
for (int i=0; i<25; i++) {
if ((i%5) == 0) putchar('\n');
printf("%+12.8f ",A[i]);
}
putchar('\n');
}
Example:
% g++ -I [OpenBlas path]/include/ example.cpp [OpenBlas path]/lib/libopenblas.a
% a.out
+0.37858900 +0.97171100 +0.01608700 +0.03766800 +0.31239800
+0.75637700 +0.34570800 +0.92294700 +0.84667100 +0.85610300
+0.73251000 +0.10894200 +0.47696900 +0.39825400 +0.50704500
+0.16260800 +0.22777000 +0.53307400 +0.80707500 +0.18033500
+0.51700600 +0.31599200 +0.91484800 +0.46082500 +0.73198000
+0.24335255 -2.67946180 +3.57538817 +0.83711880 +0.34704217
+1.02790497 -1.05086895 -0.07468137 +0.71041070 +0.66708313
-0.21087237 -4.47765165 +1.73958308 +1.73999641 +3.69324020
-0.14100897 +2.34977565 -0.93725915 +0.47383541 -2.15554470
-0.26329660 +6.46315378 -4.07721533 -3.37094863 -2.42580445
Here is a working version of Spencer Nelson's example above. One mystery about it is that the input matrix is in row-major order, even though it appears to call the underlying fortran routine dgetri. I am led to believe that all the underlying fortran routines require column-major order, but I am no expert on LAPACK, in fact, I'm using this example to help me learn it. But, that one mystery aside:
The input matrix in the example is singular. LAPACK tries to tell you that by returning a 3 in the errorHandler. I changed the 9 in that matrix to a 19, getting an errorHandler of 0 signalling success, and compared the result to that from Mathematica. The comparison was also successful and confirmed that the matrix in the example should be in row-major order, as presented.
Here is the working code:
#include <stdio.h>
#include <stddef.h>
#include <lapacke.h>
int main() {
int N = 3;
int NN = 9;
double M[3][3] = { {1 , 2 , 3},
{4 , 5 , 6},
{7 , 8 , 9} };
int pivotArray[3]; //since our matrix has three rows
int errorHandler;
double lapackWorkspace[9];
// dgetrf(M,N,A,LDA,IPIV,INFO) means invert LDA columns of an M by N matrix
// called A, sending the pivot indices to IPIV, and spitting error information
// to INFO. also don't forget (like I did) that when you pass a two-dimensional
// array to a function you need to specify the number of "rows"
dgetrf_(&N, &N, M[0], &N, pivotArray, &errorHandler);
printf ("dgetrf eh, %d, should be zero\n", errorHandler);
dgetri_(&N, M[0], &N, pivotArray, lapackWorkspace, &NN, &errorHandler);
printf ("dgetri eh, %d, should be zero\n", errorHandler);
for (size_t row = 0; row < N; ++row)
{ for (size_t col = 0; col < N; ++col)
{ printf ("%g", M[row][col]);
if (N-1 != col)
{ printf (", "); } }
if (N-1 != row)
{ printf ("\n"); } }
return 0; }
I built and ran it as follows on a Mac:
gcc main.c -llapacke -llapack
./a.out
I did an nm on the LAPACKE library and found the following:
liblapacke.a(lapacke_dgetri.o):
U _LAPACKE_dge_nancheck
0000000000000000 T _LAPACKE_dgetri
U _LAPACKE_dgetri_work
U _LAPACKE_xerbla
U _free
U _malloc
liblapacke.a(lapacke_dgetri_work.o):
U _LAPACKE_dge_trans
0000000000000000 T _LAPACKE_dgetri_work
U _LAPACKE_xerbla
U _dgetri_
U _free
U _malloc
and it looks like there is a LAPACKE [sic] wrapper that would presumably relieve us of having to take addresses everywhere for fortran's convenience, but I am probably not going to get around to trying it because I have a way forward.
EDIT
Here is a working version that bypasses LAPACKE [sic], using LAPACK fortran routines directly. I do not understand why a row-major input produces correct results, but I confirmed it again in Mathematica.
#include <stdio.h>
#include <stddef.h>
int main() {
int N = 3;
int NN = 9;
double M[3][3] = { {1 , 2 , 3},
{4 , 5 , 6},
{7 , 8 , 19} };
int pivotArray[3]; //since our matrix has three rows
int errorHandler;
double lapackWorkspace[9];
/* from http://www.netlib.no/netlib/lapack/double/dgetrf.f
SUBROUTINE DGETRF( M, N, A, LDA, IPIV, INFO )
*
* -- LAPACK routine (version 3.1) --
* Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd..
* November 2006
*
* .. Scalar Arguments ..
INTEGER INFO, LDA, M, N
* ..
* .. Array Arguments ..
INTEGER IPIV( * )
DOUBLE PRECISION A( LDA, * )
*/
extern void dgetrf_ (int * m, int * n, double * A, int * LDA, int * IPIV,
int * INFO);
/* from http://www.netlib.no/netlib/lapack/double/dgetri.f
SUBROUTINE DGETRI( N, A, LDA, IPIV, WORK, LWORK, INFO )
*
* -- LAPACK routine (version 3.1) --
* Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd..
* November 2006
*
* .. Scalar Arguments ..
INTEGER INFO, LDA, LWORK, N
* ..
* .. Array Arguments ..
INTEGER IPIV( * )
DOUBLE PRECISION A( LDA, * ), WORK( * )
*/
extern void dgetri_ (int * n, double * A, int * LDA, int * IPIV,
double * WORK, int * LWORK, int * INFO);
// dgetrf(M,N,A,LDA,IPIV,INFO) means invert LDA columns of an M by N matrix
// called A, sending the pivot indices to IPIV, and spitting error information
// to INFO. also don't forget (like I did) that when you pass a two-dimensional
// array to a function you need to specify the number of "rows"
dgetrf_(&N, &N, M[0], &N, pivotArray, &errorHandler);
printf ("dgetrf eh, %d, should be zero\n", errorHandler);
dgetri_(&N, M[0], &N, pivotArray, lapackWorkspace, &NN, &errorHandler);
printf ("dgetri eh, %d, should be zero\n", errorHandler);
for (size_t row = 0; row < N; ++row)
{ for (size_t col = 0; col < N; ++col)
{ printf ("%g", M[row][col]);
if (N-1 != col)
{ printf (", "); } }
if (N-1 != row)
{ printf ("\n"); } }
return 0; }
built and run like this:
$ gcc foo.c -llapack
$ ./a.out
dgetrf eh, 0, should be zero
dgetri eh, 0, should be zero
-1.56667, 0.466667, 0.1
1.13333, 0.0666667, -0.2
0.1, -0.2, 0.1
EDIT
The mystery no longer appears to be a mystery. I think the computations are being done in column-major order, as they must, but I am both inputting and printing the matrices as if they were in row-major order. I have two bugs that cancel each other out so things look row-ish even though they're column-ish.

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