int EVqr(MAT &A,double tol,int maxiter){
int iter = 0 ;
MAT T(A);
MAT R(A.dim()), Q(A.dim());
//since we are not able to access column of a matrix easily, so we access row of matrices as substitute.
do{
//QR decomposition
QRDecomp(T,Q,R);
T = R * Q;
iter++;
}while( (iter < maxiter) && (QRerror(T) > tol) );
printf("Largest 3 eigenvalues: ");
for (int i = 0; i < 3; i++)
printf(" %lf;",T[i][i]);
printf("\nSmallest 3 eigenvalues: ");
for (int i = A.dim()-1; i > A.dim()-4; i--)
printf(" %lf;",T[i][i]);
//printf("\niter %d\nerror: %E\n",iter,QRerror(A_k));
return iter;
}
double invPowerShift(MAT &A, double omega, VEC q, int maxIter){
int k = 1;
//I: identity matrix; LU: LU in-place;
MAT I(A.dim()), LU(A.dim()), shiftedA(A.dim());
//q:
VEC z(A.dim()), temp(A.dim()),r(A.dim()), u(A.dim()), w(A.dim());
double lambdaN= 100.0, newlambdaN=0.0, tol = 1.0;
//Form identity matrix
for (int i =0; i < A.dim(); i++){
I[i][i] = 1;
}
shiftedA = A-(omega*I);
LU = luFact(shiftedA); //LU in-place decomposition
while( (k < maxIter) && tol >= 0.000000001 ){
temp = fwdSubs(LU,q); //forward substitution
z = bckSubs(LU,temp);//backward substitution
q = z/l2norm(z);
lambdaN = q * A * q;
r = A * q - lambdaN * q;
u = q * A;
w = u/l2norm(u);
tol = l2norm(r)/std::abs(w*q);
}
//printf("Iteration: %d\n",k);
return lambdaN;
}
VEC fwdSubs(MAT &m1, VEC b) {
VEC y(b);
for(int i = 0 ; i < m1.dim() ; i++) y[i] = b[i];
for(int i = 0 ; i < m1.dim() ; i++){
for(int j = i+1 ; j<m1.dim() ; j++){
y[j] -= m1[j][i] * y[i];
}
}
return y;
}
VEC fwdSubs(MAT &m1,VEC b)//fwdSubs must be modified from the one used by LU Decomposition
{
VEC y = b;
//y[0]=b[0]/m1[0][0];
for(int i=0; i<m1.dim();i++){
//y[i]/=m1[i][i];//Add this step for modification about Cholesky method
for(int j=i+1; j<m1.dim(); j++)
y[j] -= m1[j][i]*y[i];
}
return y;
}
VEC bckSubs(MAT &m1,VEC y){
VEC x(y);
for(int i = 0 ; i < m1.dim() ; i++) x[i] = y[i];
for(int i = m1.dim()-1 ; i >= 0 ; i--){
x[i] /= m1[i][i];
for(int j = i-1 ; j >= 0 ; j--){
x[j] -= m1[j][i] *x[i];
}
}
return x;
}
VEC fwdSubs1(MAT &m1, VEC b) {
VEC y(b);
for(int i = 0 ; i < m1.dim() ; i++) y[i] = b[i];
for(int i = 0 ; i < m1.dim() ; i++){
y[i] /= m1[i][i]; //because the diagonal elements are not 1's, modify by adding this line
for(int j = i+1 ; j<m1.dim() ; j++){
y[j] -= m1[j][i] * y[i];
}
}
return y;
}
VEC operator*(VEC &v1,MAT &m1)
{
VEC v2(m1.n);
for(int i = 0; i <m1.dim();i++){
v2[i]=0;
for(int j = 0; j<m1.dim();j++){
v2[i]+=v1[j]*m1[j][i];
}
}
return v2;
}
double ConvErr(MAT A){ //find the maximum among the elements just below the diagonal
double error = 0;
for(int i=1;i<A.dim();i++){
error = max(error,fabs(A[i][i-1]));
}
return error;
}
double invpower(MAT A,VEC q,int maxiter,double eps,double &v,int aw){
double tol = 1+eps;
int k=0;
int n=A.dim();
VEC y(n);
VEC z(n);
VEC r(n);
VEC Aq(n);
VEC u(n);
VEC w(n);
MAT A1(n);
MAT S(n);
for(int i=0;i<n;i++){
S[i][i] = aw; //define the shifting matrix
}
A1 = A-S;
luFact(A1); //use LU to solve the equation
while((tol>=eps) && (k<=maxiter)){
k = k+1;
y = fwdSubs(A1,q); //forward subsitution
z = bckSubs(A1,y); //backward substitution
q = z/norm2(z);
Aq = A*q;
v = q*Aq;
r = Aq - v*q;
u = Aq;
w = u/norm2(u);
tol = norm2(r)/fabs(q*w);
}
printf("accuracy of lambda min = %e\n",norm2(r));
return k;
}
double power(MAT A,VEC q,int maxiter,double eps,double &v){
double tol = 1 + eps;
int k = 0;
int n = A.dim();
VEC z(n);
VEC r(n);
VEC Aq(n);
VEC u(n);
VEC w(n);
while((tol>=eps) && (k<=maxiter)){
Aq = A*q;
k = k+1;
q = Aq/norm2(Aq);
Aq = A*q;
v = q*Aq;
r = Aq-v*q;
u = Aq;
w = u/norm2(u);
tol = norm2(r)/fabs(q*w);
}
printf("accuracy of lambda max = %e\n",norm2(r)); //set 2-norm error of residue as accuracy term
return k;
}
// A = Q * R QR Decomposition
void QRDecomp(MAT &A,MAT &Q, MAT &R){
int n=A.dim();
MAT AT(A.tpose());
MAT QT(Q.tpose());
VEC S(n); //sum vector
R[0][0]=sqrt(AT[0]*AT[0]); //initial R
QT[0]=AT[0]/R[0][0]; //initial Q
for(int j=1;j<n;j++){
for(int i=0;i<n;i++){
S[i] = 0;
} //initialization of sum vector
for(int i=0;i<=j;i++){
R[i][j] = QT[i]*AT[j];
}
for(int i=0;i<=j-1;i++){
S = S + R[i][j]*QT[i]; //do the summation
}
S = AT[j] - S;
R[j][j]=sqrt(S*S);
QT[j] = S/R[j][j];
}
Q=QT.tpose();
}
int EVqrShifted(MAT &A,double mu, double tol,int maxiter){
int n=A.dim();
int k=0;
double err=10;
MAT Q(n),R(n),T(A);
while(err>tol && k<maxiter){
for(int i=0;i<n;i++){ //subtract mu*I before QR factorization
T[i][i] = T[i][i] - mu;
}
QRDecomp(T,Q,R); //QR factorization
T=R*Q; //matrix multiplication
for(int i=0;i<n;i++){ //add mu*I back
T[i][i] = T[i][i] + mu;
}
err = ConvErr(T);
k++;
}
printf("the three largest EV:\n");
printf("%lf %lf %lf\n",T[0][0],T[1][1],T[2][2]);
printf("the three smallest EV:\n");
printf("%lf %lf %lf\n",T[n-1][n-1],T[n-2][n-2],T[n-3][n-3]);
printf("iter = %d\n",k);
printf("error = %e\n",err);
A=T;
return k;
}
double invPowerShift(MAT &A, double omega, VEC q, int maxIter){
int k = 1;
//I: identity matrix; LU: LU in-place; iLU: LU in-place for invA; invA: inverse of A
MAT I(A.dim()), LU(A.dim()),iLU(A.dim()), invA = inverse(A), shiftedA(A.dim());
//q:
VEC z(A.dim()), temp(A.dim());// q(A.dim()),iq(A.dim()), iz(A.dim()),
double lambdaN= 100.0, newlambdaN=0.0;
//Form identity matrix
for (int i =0; i < A.dim(); i++){
I[i][i] = 1;
}
shiftedA = A-(omega*I);
LU = luFact(shiftedA); //LU in-place decomposition
//iLU = luFact(invA-(w*I)); //LU in-place decomposition
while( (k < maxIter) && (std::abs(newlambdaN - lambdaN) >= 10E-9) ){
lambdaN = newlambdaN;
temp = fwdSubs(LU,q); //forward substitution
z = bckSubs(LU,temp);//backward substitution
q = z/l2norm(z);
newlambdaN = q * A * q;
/*
temp = fwdSubs(iLU,iq); //forward substitution
iz = bckSubs(iLU,temp);//backward substitution
iq = iz / l2norm(iz);
imu = iq * invA * iq;
//newCond = mu/imu;
*/
}
printf("Iteration: %d\n",k);
return newlambdaN;
}
double powerMethod(MAT &A, VEC q, int maxIter){
VEC z(A.dim()), r(A.dim()), u(A.dim()), w(A.dim());
MAT A_k(A.dim());
double lambda1, tol= 1.0;
//double new_lambda
int iter = 0;
while( (iter < maxIter) && (tol >= 0.000000001) ){
z = A * q;
iter++;
q = z/l2norm(z);
lambda1 = q * A * q;
r = A * q - lambda1 * q;
u = q * A;
w = u/l2norm(u);
tol = l2norm(r)/std::abs(w*q);
}
return lambda1;
}
VEC bckSubs(MAT &m1,VEC b)//b as y
{
int i,j,k;
VEC x=b;
for(i=m1.dim()-1 ;i>=0 ;i--){
x[i] /= m1[i][i];
for(j=i-1; j>=0; j--)
x[j] -= m1[j][i] * x[i];
}
return x;
}
double invPowerShift(MAT &A, double omega, VEC q, int maxIter){
int k = 1;
//I: identity matrix; LU: LU in-place;
MAT I(A.dim()), LU(A.dim()), shiftedA(A.dim());
//q:
VEC z(A.dim()), temp(A.dim());// q(A.dim())
double lambdaN = 100.0, newlambdaN=0.0;
//Form identity matrix
for (int i =0; i < A.dim(); i++){
I[i][i] = 1;
}
shiftedA = A-(omega*I);
LU = luFact(shiftedA); //LU in-place decomposition
while( (k < maxIter) && (std::abs(newlambdaN - lambdaN) >= 0.000000001) ){
lambdaN = newlambdaN;
temp = fwdSubs(LU,q); //forward substitution
z = bckSubs(LU,temp);//backward substitution
q = z/l2norm(z);
newlambdaN = q * A * q;
}
printf("Iteration: %d\n",k);
return newlambdaN;
}
int gaussSeidel(MAT &A,VEC b,VEC &x,int maxIter,double tol)
{
int k = 0;
double theta=0.0;
VEC newx(A.dim()),ans(A.dim()),temp(A.dim());
MAT LU(A.dim()),a = A;
LU = luFact(a); //LU in-place decomposition
temp=fwdSubs(LU,b); //forward substitution
ans= bckSubs(LU,temp);//backward substitution
while( k < maxIter)
{
x = newx;
for(int i=0; i< A.dim(); i++)
{
theta = 0.0;
for(int j=0; j < A.dim();j++){
if(i!=j){
theta += (A[i][j]*newx[j]);
}
}
newx[i]= (b[i]-theta)/A[i][i];
}
k++;
if (linfnorm(newx - x) < tol)
break;
}
printf("Diffrence w.r.t. hw4: %E\n",linfnorm(newx-ans));
return k;
}
int cg(MAT &A,VEC b,VEC &x,int maxIter, double tol)
{
//A_p: A * p
VEC A_p(A.dim());
int node = std::sqrt(A.dim());
//p: search direction;
VEC p(A.dim()), r(A.dim()), newr(A.dim()), newx(A.dim());//,ans(A.dim()),temp(A.dim());
//MAT LU(A.dim()),a = A;
//r2: rT * r
double alpha, beta, r2, newr2, err;//,g;
//g = (double)(node-1)/2000.0;
int iter = 0;//
/*
LU = luFact(a); //LU in-place decomposition
temp=fwdSubs(LU,b); //forward substitution
ans= bckSubs(LU,temp);//backward substitution
*/
//Initial condition for CGM
p = r = b - (A * x);
r2 = r * r;
while(iter < maxIter)
{
A_p = A * p;
alpha = r2 / (p * A_p);
newx = x + (alpha * p);
err = std::sqrt(r2/A.dim());
if ( err < tol )
break;
newr = r - (alpha * A_p);
newr2 = newr * newr;
beta = newr2 / r2;
p = newr + beta * p;
//Re-initialization for next iteration
x = newx;
r = newr;
r2 = newr2;
//////////////////////////////////////
iter++;
}
//printf("cg:Vne: %lf; Vsw: %lf; Vse: %lf; R:%lf\n",newx[node-1],newx[(node-1)*node],newx[node*node-1], std::abs( 1/(g*(newx[0]-newx[1])+g*(newx[0]-newx[node] )) ) );
//printf("LU:Vne: %lf; Vsw: %lf; Vse: %lf; R:%lf\n",ans[node-1],ans[(node-1)*node],ans[node*node-1], std::abs( 1/(g*(ans[0]-ans[1])+g*(ans[0]-ans[node] )) ) );
//printf("Difference w.r.t. hw4: %E\n",linfnorm(ans - newx));
return iter;
}
int cg(MAT &A,VEC b,VEC &x,int maxIter,double tol){
int n = A.dim();
int k = 0;
VEC x_next(n);
VEC r_next(n);
VEC p_next(n);
VEC r(n);
VEC p(n);
MAT L(n);
VEC cpr(n);
double alpha,beta;
double err;
double diff;
r = b - A*x; //initial condition
p = r;
while(k<maxIter){ //conjugate gradient decent
alpha = (r*r)/(p*(A*p));
x_next = x + alpha*p;
r_next = r - alpha*(A*p);
beta = (r_next*r_next)/(r*r);
p_next = r_next + beta*p;
k++;
x = x_next; //assign to the x,r,p of the next iteration
r = r_next;
p = p_next;
err = pow((r*r)/n,0.5);
if(err<tol) //see if the error is smaller than the defined tolerance. if so, break out of the loop
break;
}
diff = 0;
//the answer from hw4
L = cholesky(A); //in-place Cholesky Decomposition
cpr = choSolve(L,b); //solve the linear system by forward and backward substitution
//use the same method to compute the error between hw4 and hw5, see if < 10^-7. if not, decrease the tol
for(int i=0;i<n;i++){
diff = max(diff,fabs(x[i]-cpr[i])); //use infinity norm to compute the error
}
printf("error between hw4 and hw6(infinity-norm): %e\n",diff);
return k;
}
int EVqr(MAT &A,double tol,int maxiter){
int n=A.dim();
int k=0;
double err=10;
MAT Q(n),R(n),T(A);
while(err>tol && k<maxiter){
QRDecomp(T,Q,R); //QR factorization
T=R*Q; //matrix multiplication
err = ConvErr(T); //get the error term in each iteration
k++;
}
printf("the three largest EV:\n");
printf("%lf %lf %lf\n",T[0][0],T[1][1],T[2][2]);
printf("the three smallest EV:\n");
printf("%lf %lf %lf\n",T[n-1][n-1],T[n-2][n-2],T[n-3][n-3]);
printf("iter = %d\n",k);
printf("error = %e\n",err);
A=T;
return k;
}
int EVqrShifted(MAT &A,double mu,double tol,int maxiter){
int iter = 0 ;
MAT T(A);
MAT R(A.dim()), Q(A.dim()),muI(A.dim());
//since we are not able to access column of a matrix easily, so we access row of matrices as substitute.
for (int i =0 ; i < A.dim(); i++)
muI[i][i] = mu;
do{
for(int i=0;i<A.dim();i++){ //subtract mu*I before QR factorization
T[i][i] = T[i][i] - mu;
}
//QR decomposition
QRDecomp(T,Q,R);
T = R * Q;
//QR decomposition ends
for(int i=0;i<A.dim();i++){ //add mu*I back
T[i][i] = T[i][i] + mu;
}
iter++;
}while( (iter < maxiter) && (QRerror(T) > tol) );
printf("Largest 3 eigenvalues: ");
for (int i = 0; i < 3; i++)
printf(" %lf;",T[i][i]);
printf("\nSmallest 3 eigenvalues: ");
for (int i = A.dim()-1; i > A.dim()-4; i--)
printf(" %lf;",T[i][i]);
//printf("\niter %d\nerror: %E\n",iter,QRerror(A_k));
return iter;
}
VEC choSolve(MAT &L,VEC b)
{
VEC x(b);
//forward substitutions
for (int i=0; i<x.len(); i++)
{
x[i] /= L[i][i];
for (int j=i+1; j<L.dim(); j++)
x[j] -= L[j][i]*x[i];
}
//backward substitutions
for (int i=(x.len()-1); i>=0; i--)
{
x[i] /= L[i][i];
for (int j=i-1; j>=0; j--)
x[j] -= L[i][j]*x[i];
}
return x;
}
MAT inverse(MAT &A)
{
VEC temp(A.dim());
// I: identity matrix; inv: inverse of A; LU: LU in-place
MAT LU(A.dim()), a = A, I(A.dim()), inv(A.dim());
LU = luFact(a); //LU in-place decomposition
// Form identity matrix
for (int i =0; i < A.dim(); i++){
I[i][i] = 1;
}
// solve inverse of A. Answer is stored row by row
for (int i = 0; i< A.dim(); i++){
temp=fwdSubs(LU,I[i]); //forward substitution
inv[i]= bckSubs(LU,temp);//backward substitution
}
// we need the result of inverse() column by column, so return transpose matrix of inv
return inv.tpose();
}
double QRerror(MAT &A){
double max = std::abs(A[1][0]);
for (int i = 2; i< A.dim(); i++)
max = std::max(std::abs(A[i][i-1]),max);
return max;
}
int jacobi(MAT &A,VEC b,VEC &x, int maxIter, double tol){
int n = A.dim();
VEC S(n); //summation vector
VEC G(n); //diagonal of A
VEC tmp(n); //save the x before iterative method
MAT L(n);
VEC cpr(n); //the result of hw4
//iterative
int k=0;
double err1;
double err2;
double errinf;
double diff1,diff2,diffinf;
while(k<maxIter){
for(int i=0;i<n;i++){
S[i] = 0; //initialize the sum vector to 0
}
for(int i=0;i<n;i++){
G[i] = A[i][i];
//save the previous vector x to tmp(for comparison between iteration k and k+1)
tmp[i] = x[i];
for(int j=0;j<n;j++){
if(j!=i){
S[i] = S[i] + A[i][j]*x[j]; //sum the Aij*xj, where i is not equal to j
}
}
}
for(int i=0;i<n;i++){
x[i] = (1/G[i])*(b[i]-S[i]); //compute the new solution of x at iteration k
}
k++; //after each iteration, k=k+1
err1 = 0; //1-norm
err2 = 0; //2-norm
errinf = 0; //infinity-norm
for(int i=0;i<n;i++){
err1 += fabs(x[i] - tmp[i]); //sum of absolute error
err2 += pow(x[i] - tmp[i], 2); //sum of square error
errinf = max(errinf,fabs(x[i]-tmp[i])); //take the maximum absolute difference as error
} //max(a,b) defined on the top
err2 = pow(err2,0.5); //find the square root of err2 and get the 2-norm
//you can change err1 to err2 or errinf to get different number of iterations k based on which p-norm you preferred
if(err1<tol){
break;
}
}
diff1 = 0;
diff2 = 0;
diffinf = 0;
//the answer from hw4
L = cholesky(A); //in-place Cholesky Decomposition
cpr = choSolve(L,b); //solve the linear system by forward and backward substitution
//use the same method to compute the error between hw4 and hw5, see if < 10^-7. if not, decrease the tol
for(int i=0;i<n;i++){
diff1 += fabs(x[i] - cpr[i]);
diff2 += pow(x[i] - cpr[i], 2);
diffinf = max(diffinf,fabs(x[i]-cpr[i]));
}
diff2 = pow(diff2,0.5);
printf("error between hw4 and hw5(1-norm 2-norm infinity-norm): %e %e %e\n",diff1,diff2,diffinf);
return k;
}
int gaussSeidel(MAT &A,VEC b,VEC &x,int maxIter,double tol)
{
int n = A.dim();
VEC S(n);
VEC G(n);
VEC tmp(n);
MAT L(n);
VEC cpr(n);
//iterative
int k=0;
double err1;
double err2;
double errinf;
double diff1,diff2,diffinf;
while(k<maxIter){
for(int i=0;i<n;i++){
S[i] = 0;
}
for(int i=0;i<n;i++){
G[i] = A[i][i];
tmp[i] = x[i];
for(int j=0;j<n;j++){
if(j!=i){
S[i] = S[i] + A[i][j]*x[j];
}
}
x[i] = (1/G[i])*(b[i]-S[i]);
//both x[i] and S[i] are computed in the same for loop, so some updated values are used in the current iteration
}
k++;
err1 = 0;
err2 = 0;
errinf = 0;
for(int i=0;i<n;i++){
err1 += fabs(x[i] - tmp[i]);
err2 += pow(x[i] - tmp[i], 2);
errinf = max(errinf,fabs(x[i]-tmp[i]));
}
err2 = pow(err2,0.5);
if(err1<tol){
break;
}
}
diff1 = 0;
diff2 = 0;
diffinf = 0;
//the answer from hw4
L = cholesky(A); //in-place Cholesky Decomposition
cpr = choSolve(L,b); //solve the linear system by forward and backward substitution
//use the same method to compute the error between hw4 and hw5, see if < 10^-7. if not, decrease the tol
for(int i=0;i<n;i++){
diff1 += fabs(x[i] - cpr[i]);
diff2 += pow(x[i] - cpr[i], 2);
diffinf = max(diffinf,fabs(x[i]-cpr[i]));
}
diff2 = pow(diff2,0.5);
printf("error between hw4 and hw5(1-norm 2-norm infinity-norm): %e %e %e\n",diff1,diff2,diffinf);
return k;
}
int sgs(MAT &A,VEC b,VEC &x,int maxIter,double tol)
{
int n = A.dim();
VEC S(n);
VEC G(n);
VEC tmp(n);
MAT L(n);
VEC cpr(n);
//iterative
int k=0;
double err1;
double err2;
double errinf;
double diff1,diff2,diffinf;
while(k<maxIter){
for(int i=0;i<n;i++){
S[i] = 0;
}
//forward gauss-seidel
//compute vector x from x[0] to x[n-1]
for(int i=0;i<n;i++){
G[i] = A[i][i];
tmp[i] = x[i];
for(int j=0;j<n;j++){
if(j!=i){
S[i] = S[i] + A[i][j]*x[j];
}
}
x[i] = (1/G[i])*(b[i]-S[i]);
}
//backward gauss-seidel
//use the reslults of forward gauss-seidel to compute vector x
//direction : from x[n-1] to x[0]
for(int i=0;i<n;i++){
S[i] = 0; //we also need to initialize the sum vector.
}
for(int i=n-1;i>=0;i--){
for(int j=n-1;j>=0;j--){
if(j!=i){
S[i] = S[i] + A[i][j]*x[j];
}
}
x[i] = (1/G[i])*(b[i]-S[i]);
}
k++;
err1 = 0;
err2 = 0;
errinf = 0;
for(int i=0;i<n;i++){
err1 += fabs(x[i] - tmp[i]);
err2 += pow(x[i] - tmp[i], 2);
errinf = max(errinf,fabs(x[i]-tmp[i]));
}
err2 = pow(err2,0.5);
if(err1<tol){
break;
}
}
diff1 = 0;
diff2 = 0;
diffinf = 0;
//the answer from hw4
L = cholesky(A); //in-place Cholesky Decomposition
cpr = choSolve(L,b); //solve the linear system by forward and backward substitution
//use the same method to compute the error between hw4 and hw5, see if < 10^-7. if not, decrease the tol
for(int i=0;i<n;i++){
diff1 += fabs(x[i] - cpr[i]);
diff2 += pow(x[i] - cpr[i], 2);
diffinf = max(diffinf,fabs(x[i]-cpr[i]));
}
diff2 = pow(diff2,0.5);
printf("error between hw4 and hw5(1-norm 2-norm infinity-norm): %e %e %e\n",diff1,diff2,diffinf);
return k;
}
