this is a piece of code for a simple iteration method for solving systems of linear algebraic equations:
double* iter(double** a, double* y, int n, int& iter)
{
double* res = new double[n];
int i, j;
for (i = 0; i < n; i++)
{
res[i] = y[i] / a[i][i];
}
double eps = 0.0001;
double* Xn = new double[n];
do {
iter++;
for (i = 0; i < n; i++) {
Xn[i] = y[i] / a[i][i];
for (j = 0; j < n; j++) {
if (i == j)
continue;
else {
Xn[i] -= a[i][j] / a[i][i] * res[j];
}
}
}
bool flag = true;
for (i = 0; i < n - 1; i++) {
if (fabs(Xn[i] - res[i]) > eps) {
flag = false;
break;
}
}
for (i = 0; i < n; i++) {
res[i] = Xn[i];
}
if (flag)
break;
} while (1);
return res;
}
and formula for it:
but I would like to implement the seidel method.and slightly changed the code according to the formula below
for (i = 0; i < n; i++) {
Xn[i] = y[i] / a[i][i];
for (j = 0; j < i-1; j++) {
Xn[i] -= a[i][j] / a[i][i] * Xn[j];
}
for (j = i+1; j < n; j++){
Xn[i] -= a[i][j] / a[i][i] * res[j];
}
}
but I'm not getting exactly what I expected:
I would be grateful if you could tell me where I made a mistake. thank you in advance for your answers.
Your mistake lies in the new implementation.
The first sum of the Seidel method sums up to the element before the diagonal, while your for loop goes up to two elements before the diagonal.
Instead of
for(j = 0; j < i-1; j++)
you should have
for(j = 0; j < i; j++)
Note that Gauss Seidel method is applicable if the elements on the diagonal are non-zero.
Related
enter image description here
i have this expression of a tour of worker and i tried to coded it and i don't know if it is correct or no because i have many errors so can someone help me ,
X[w][w][i][j] is a decision variable , p [i][j] is the weight of processing arc from i to j and d[i][j] is a distance
this is my proposition of code
compteur = 0;
IloFloatVarArray2 CW(env, W);
for (w = 0; w < W; w++)
{
CW[w] = IloFloatVarArray(env, W, 0.0, INFINITY);
model.add(CW[w]);
#ifdef DEBUG
for (w = 0; w < W; w++)
{
sprintf(varname, "CW_%d_%d", w, w);
CW[w][w].setName(varname);
compteur++;
}
}
#endif
#ifdef DEBUG
printf("compteur cw =%d\n", compteur);
#endif
IloExpr CW[w][w](env);
for (i = 0; i < A; i++)
for (j = 0; j < A; j++)
CW[w][w] += d[i][j] * xW[w][i][j][w];
for (i = 0; i < A; i++)
for (j = 0; j < A; j++)
CW[w][w] += Parc[i][j] * xW[w][i][j][w];
for (i = 0; i < A; i++)
for (j = 0; j < A; j++)
CW[w][w] += 1 * xW[w][i][j][w];
model.add(env, CW[w][w]);
CW[w][w].end();
you have to create a 2-D array of expressions, fill it, and then constrain the expressions by some constraints. For example:
#include <ilcp/cp.h>
int main() {
typedef IloArray<IloNumExprArray> IloNumExprArray2;
IloEnv env;
IloInt n = 4;
IloNumExprArray2 c(env, n);
IloIntVarArray var(env, n, 0, n-1);
for (IloInt x = 0; x < n; x++) {
c[x] = IloNumExprArray(env, n);
for (IloInt y = 0; y < n; y++) {
c[x][y] = IloNumExpr(env, 0);
c[x][y] += var[x] * var[y];
}
}
IloModel mdl(env);
for (IloInt x = 0; x < n; x++) {
for (IloInt y = 0; y < n; y++) {
mdl.add(c[x][y] ==1);
}
}
IloCP cp(mdl);
cp.startNewSearch();
while (cp.next()) {
cp.out() << cp.domain(var) << std::endl;
}
cp.end();
env.end();
return 0;
}
I wrote the code according to the algorithm, but the result is incorrect. According to the algorithm, we must indicate the dimension of the matrix and manually fill in the main matrix A and vector B. We need to generate an LU matrix. It is generated, but with the wrong numbers. And in the end we have to get the vector X with solutions. And this is in windowed mode.
https://imgur.com/TSsjMXp
int N = 1; // matrix dimension
double R = 0;
typedef double Matrix [6][6];
typedef double Vec [6];
.
.
.
void Decomp (Matrix A, int N, int &Change)
{
int i, j, k ;
double R, L, U;
Change = 1;
R = Math::Abs(A[1][1]);
for(j=2; j<=N; j++)
if (Math::Abs(A[j][1])>= R)
{
Change = j;
R = Math::Abs(A[j][1]);
}
if (R<= 1E-7)
{
MessageBox::Show("The system is degenerate");
}
if (k!=1)
{
for(i=1; i<=N; i++)
{
R = A[Change][i];
A[Change][i] = A[1][i];
A[1][i] = R;
}
}
for(i=2; i<=N; i++)
A[1][i] = A[1][i]/A[1][1];
for(i=2; i<=N; i++)
{
for(k=i; k<=N; k++);
{
R = 0;
for ( j=1; j<=(i-1); j++)
R = R + A[k][j] * A[j][i];
A[k][i] = A[k][i] - R;
}
if (A[i][i]<= 1E-7)
{
MessageBox::Show("The system is degenerate[enter image description here][1]");
}
for(k = i+1; k<=N; k++)
{
R = 0;
for (j=1; j<=(i-1); j++)
R = R + A[i][j] * A[j][k];
A[i][k] = (A[i][k] - R) / A[i][i];
}
}
for (int i = 0; i < N; i++)
for (int j = 0; j < N; j++)
{
C_matrix_dgv->Rows[i]->Cells[j] -> Value = Convert::ToString(A[i+1][j+1]);
}
}
void Solve (Matrix A, Vec b, Vec x, int Change, int N)
{
int i = 0,j = 0;
double R;
if (Change!=1)
{
R = b[Change];
b[Change] = b[1];
b[1] = R;
}
b[1] = b[1]/A[1][1];
for(i=2; i<=N; i++)
{
R = 0;
for( j=1; j<=(i-1); j++)
R = R + A[i][j] * b[j];
b[i] = (b[i] - R) / A[i][i];
}
x[N] = b[N];
for( i=1; i<=(N-1); i++)
{
R = 0;
for(j = (N+1-i); j<=N; j++)
R = R + A[N - i][j] * x[j];
x[N - i] = b[N - i] - R;
}
}
int N = 1; // matrix dimension
If you use this in the rest of the code you cannot get correct results. The dimension of the matrix is 6x6. Use a std::array or std::vector so that you dont need to keep the size in a seperate variable.
I'm trying to implement a quick program to solve a system of linear equations. The program reads the input from a file and then writes the upper-triangular system and solutions to a file. It is working with no pivoting, but when I try to implement the pivoting it produces incorrect results.
As example input, here is the following system of equations:
w+2x-3y+4z=12
2w+2x-2y+3z=10
x+y=-1
w-x+y-2z=-4
I expect the results to be w=1, x=0, y=-1 and z=2. When I don't pivot, I get this answer (with some rounding error on x). When I add in the pivoting, I get the same numbers but in the wrong order: w=2,x=1,y=-1 and z=0.
What do I need to do to get these in the correct order? Am I missing a step somewhere? I need to do column swapping instead of rows because I need to adapt this to a parallel algorithm later that requires that. Here is the code that does the elimination and back substitution:
void gaussian_elimination(double** A, double* b, double* x, int n)
{
int maxIndex;
double temp;
int i;
for (int k = 0; k < n; k++)
{
i = k;
for (int j = k+1; j < n; j++)
{
if (abs(A[k][j]) > abs(A[k][i]))
{
i = j;
}
}
if (i != k)
{
for (int j = 0; j < n; j++)
{
temp = A[j][k];
A[j][k] = A[j][i];
A[j][i] = temp;
}
}
for (int j = k + 1; j < n; j++)
{
A[k][j] = A[k][j] / A[k][k];
}
b[k] = b[k] / A[k][k];
A[k][k] = 1;
for (i = k + 1; i < n; i++)
{
for (int j = k + 1; j < n; j++)
{
A[i][j] = A[i][j] - A[i][k] * A[k][j];
}
b[i] = b[i] - A[i][k] * b[k];
A[i][k] = 0;
}
}
}
void back_substitution(double**U, double*x, double*y, int n)
{
for (int k = n - 1; k >= 0; k--)
{
x[k] = y[k];
for (int i = k - 1; i >= 0; i--)
{
y[i] = y[i] - x[k]*U[i][k];
}
}
}
I believe what you implemented is actually complete pivoting.
With complete pivoting, you must keep track of the permutation of columns, and apply the same permutation to your answer.
You can do this with an array {0, 1, ..., n}, where you swap the i'th and k'th values in the second loop. Then, rearange the solution using this array.
If what you were trying to do is partial pivoting, you need to look for the maximum in the respective row, and swap the rows and the values of 'b' accordingly.
all the arrays in this code are complex type in this code and the running time for this for loop is about 1 min. Ktemp is an array with size 141*1202*141. could anyone help me to optimize this code and save the running time?
complex<double> ***P1;
P1 = new complex<double>**[141];
for (i = 0; i < num_y; i++)
{
P1[i] = new complex<double> *[1202];
for (j = 0; j < tsize; j++)
{
P1[i][j] = new complex<double>[141];
}
}
for (int zz = 1; zz < 20; zz++)//in z direction
{
for (i = 0; i < 141; i++)
{
for (j = 0; j < 1202; j++)
{
for (k = 0; k < 141; k++)
{
if (Ktemp[i][j][k].real() <= 0)
{
P1[i][j][k] = 0;
}
else
{
P1[i][j][k] = excit_pfft[i][j][k] * expn[i][j][k];
}
}
}
}
excit_pfft = P1;
}
my second question is about rewriting matlab function 'fftshift' with C++. I have finished the code, but it seems not that efficient. could anyone help me rewrite this code? my code is attached below:
complex<double> ***fftw_shift(complex<double> ***te, int a, int b, int c)
{
complex<double> ***tempa;
tempa = new complex<double> **[a];
for (i = 0; i < a; i++)
{
tempa[i] = new complex<double> *[b];
for (j = 0; j < b; j++)
{
tempa[i][j] = new complex<double>[c];
}
}
/*for the row*/
if (c % 2 == 1)
{
for (i = 0; i < a; i++)
{
for (j = 0; j < b; j++)
{
for (k = 0; k < c / 2; k++)
{
tempa[i][j][k] = te[i][j][k + c / 2 + 1];
tempa[i][j][k + c / 2] = te[i][j][k];
tempa[i][j][c - 1] = te[i][j][c / 2];
}
}
}
}
else
{
for (i = 0; i < a; i++)
{
for (j = 0; j < b; j++)
{
for (k = 0; k < c / 2; k++)
{
tempa[i][j][k] = te[i][j][k + c / 2];
tempa[i][j][k + c / 2] = te[i][j][k];
}
}
}
}
for (i = 0; i < a; i++)
{
for (j = 0; j < b; j++)
{
for (k = 0; k < c; k++)
{
te[i][j][k] = tempa[i][j][k];
}
}
}
/*for the column*/
if (b % 2 == 1)
{
for (i = 0; i < a; i++)
{
for (j = 0; j < b / 2; j++)
{
for (k = 0; k < c; k++)
{
tempa[i][j][k] = te[i][j + b / 2 + 1][k];
tempa[i][j + b / 2][k] = te[i][j][k];
tempa[i][b - 1][k] = te[i][b / 2][k];
}
}
}
}
else
{
for (i = 0; i < a; i++)
{
for (j = 0; j < b / 2; j++)
{
for (k = 0; k < c; k++)
{
tempa[i][j][k] = te[i][j + b / 2][k];
tempa[i][j + b / 2][k] = te[i][j][k];
}
}
}
}
for (i = 0; i < a; i++)
{
for (j = 0; j < b; j++)
{
for (k = 0; k < c; k++)
{
te[i][j][k] = tempa[i][j][k];
}
}
}
/*for the third dimension*/
if (a % 2 == 1)
{
for (i = 0; i < a / 2; i++)
{
for (j = 0; j < b; j++)
{
for (k = 0; k < c; k++)
{
tempa[i][j][k] = te[i + a / 2 + 1][j][k];
tempa[i + a / 2][j][k] = te[i][j][k];
tempa[a - 1][j][k] = te[a / 2][j][k];
}
}
}
}
else
{
for (i = 0; i < a / 2; i++)
{
for (j = 0; j < b; j++)
{
for (k = 0; k < c; k++)
{
tempa[i][j][k] = te[i + a / 2][j][k];
tempa[i + a / 2][j][k] = te[i][j][k];
}
}
}
}
for (i = 0; i < a; i++)
{
for (j = 0; j < b; j++)
{
for (k = 0; k < c; k++)
{
te[i][j][k] = tempa[i][j][k];
}
}
}
return (te);
}
Since you are repeatedly multiplying by the values in expn (i.e. calculating an exponent) you can do this more efficiently using the pow function and get rid of the zz loop:
for (i = 0; i < 141; i++)
{
for (j = 0; j < 1202; j++)
{
for (k = 0; k < 141; k++)
{
if (Ktemp[i][j][k].real() <= 0)
{
excit_pfft[i][j][k] = 0;
}
else
{
excit_pfft[i][j][k] = excit_pfft[i][j][k] * pow(expn[i][j][k], 20);
}
}
}
}
Your code also seems to have a memory leak because you assign P1 to excit_pfft, but never free the previous contents of excit_pfft. You don't need to have the P1 temporary array in any case once you get rid of the outer loop.
I'm not sure of the internals of the complex pow() function, but you can calculate the (scalar) exponent of a complex number geometrically by converting it to polar co-ordinates (angle + distance scalar), then multiplying the angle by the power and raising the distance to the power, then converting back. So it's a lot faster than repeated multiplication.
First (will probably give you a big performance boost), get rid of the pointer arrays if you know beforehand the size of your arrays and simply allocate them in the stack:
complex<double> P1[141][1202][141];
Instead of :
complex<double> ***P1;
P1 = new complex<double>**[141];
for (i = 0; i < num_y; i++)
{
P1[i] = new complex<double> *[1202];
for (j = 0; j < tsize; j++)
{
P1[i][j] = new complex<double>[141];
}
}
And since I don't know exactly what this does, I'm assuming this:
for (int zz = 1; zz < 20; zz++)//in z direction
{
for (i = 0; i < 141; i++)
{
for (j = 0; j < 1202; j++)
{
for (k = 0; k < 141; k++)
{
if (Ktemp[i][j][k].real() <= 0)
{
P1[i][j][k] = 0;
}
else
{
P1[i][j][k] = excit_pfft[i][j][k] * expn[i][j][k];
}
}
}
}
excit_pfft = P1;
}
Could become this:
for (int zz = 1; zz < 20; zz++)//in z direction
{
for (i = 0; i < 141; i++)
{
for (j = 0; j < 1202; j++)
{
for (k = 0; k < 141; k++)
{
if (Ktemp[i][j][k].real() <= 0)
{
P1[i][j][k] = 0;
}
else
{
P1[i][j][k] = P1[i][j][k] * expn[i][j][k];
}
}
}
}
}
If this cannot be done than I'll need a more broad chunk of this code to analyze excit_pfft, etc.
A huge performance boost you could have is to use Worker Threads and run this last code multithreaded.
The same goes for our second question, Worker Threads should do it.
EDIT:
On second though, the stack won't be able to handle that much variables.
I'd recommend using vector<vector<vector<complex<double> > > > instead.
Closed. This question needs debugging details. It is not currently accepting answers.
Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question.
Closed 7 years ago.
Improve this question
void power(int a[][21], int n, int d) {
int e[21][21], k, i, j, l;
if (d == 1) {
for (i = 1; i <= n; i++) {
for (j = 1; j <= n; j++)
cout << a[i][j] << " ";
cout << endl;
}
} else {
for (i = 1; i <= n; i++)
for (j = 1; j <= n; j++)
e[i][j] = 0;
for (l = 1; l < d; l++)
for (i = 1; i <= n; i++)
for (j = 1; j <= n; j++)
for (k = 1; k <= n; k++)
e[i][j] = e[i][j] + (a[i][k] * a[k][j]);
for (i = 1; i <= n; i++) {
for (j = 1; j <= n; j++)
cout << e[i][j] << " ";
cout << endl;
}
}
}
I am trying to build a function that calculates the power of a matrix. The program works for the 1st and 2nd power of the matrix but if I want to calculate the 3rd power the function will return incorrect values. I think the problem is at retaining the previous results but I can't figure out on how to fix it.
This block of code
for (l = 1; l < d; l++)
for (i = 1; i <= n; i++)
for (j = 1; j <= n; j++)
for (k = 1; k <= n; k++)
e[i][j] = e[i][j] + (a[i][k] * a[k][j]);
sets e to be a*a no matter what d is.
You need to have a temporary matrix to make things work.
Bootstrap:
e = a;
In the loop:
temp = e;
e = temp * a;
temp = e;
e = temp * a;
... etc.
Something along the lines of:
for (i = 0; i < n; i++)
{
for (j = 0; j < n; j++)
{
e[i][j] = a[i][j];
}
}
for (l = 1; l < d; l++)
{
for (i = 0; i < n; i++)
{
for (j = 0; j < n; j++)
{
temp[i][j] = e[i][j];
}
}
for (i = 0; i < n; i++)
{
for (j = 0; j < n; j++)
{
e[i][j] = 0;
for (k = 0; k < n; k++)
{
e[i][j] += (temp[i][k] * a[k][j]);
}
}
}
}
PS I have used 0-based indexing to access the matrices. I am not sure why you have used 1-based indexing.