static void B5()
{
WriteLine("\n Problem B5\n");
int n = 5;
matrix A = rand_sym_mat(n);
matrix B = A.copy();
matrix C = A.copy();
WriteLine($"Random symmetric matrix A with size {n}");
WriteLine("We find all eigenvalues with cyclic routine");
jcbi_cycl test_cycl = new jcbi_cycl(B);
test_cycl.Eigvals.print("Eigenvalues found by cyclic routine, low to high");
WriteLine("\nNow we use the value-by-value routine to calculate eigenvalues from last to first");
WriteLine("We do this by changing the calculation of " +
"rotation angle theta to 0.5*Atan2(-Apq, App-Aqq)\n");
bool invert = true;
int evalnum = n;
bool vbv = true;
jcbi_cycl test_vbv = new jcbi_cycl(C, vbv, evalnum, invert);
test_vbv.Eigvals.print("Eigenvalues value-by-value, in order of calculation");
}
static void B2()
{
WriteLine("\n Problem B2\n");
int n = 8;
matrix A = rand_sym_mat(n);
matrix B = A.copy();
WriteLine("Random symmetric matrix A with size {0}", n);
WriteLine("Perform eigenvalue by eigenvalue calculation for first 4 values");
int evalnum = 4;
bool val_by_val = true;
jcbi_cycl test_vbv = new jcbi_cycl(B, val_by_val, evalnum);
Write("Found eigenvalues = ");
for (int i = 0; i < evalnum; i++)
{
Write(" {0:g3} ", test_vbv.Eigvals[i]);
}
Write("\n");
((test_vbv.V).T * A * test_vbv.V - test_vbv.D).print("V.T*A*V - D = ", "{0,10:f6}");
WriteLine("\n Total rotations used = {0}", test_vbv.Rotations);
B.print("\n Matrix copy used, after value by value operations = ", "{0,10:f6}");
matrix C = A.copy();
WriteLine("\nSimilar calculation with cyclic sweeps");
jcbi_cycl test_cycl = new jcbi_cycl(C);
test_cycl.Eigvals.print("Found eigenvalues = ");
(test_cycl.V.T * A * test_cycl.V - test_cycl.D).print("V.T*A*V - D = ", "{0,10:f6}");
WriteLine("\n Total rotations used = {0}", test_cycl.Rotations);
C.print("\n Matrix copy used, after cyclic sweeps = ", "{0,10:f6}");
}
public static void Main(){
// for(int n=2;n<50;n+=5){
for(int n=2;n<50;n+=10){
vector e=new vector(n);
matrix V=new matrix(n,n);
var rnd = new Random(1);
matrix A = new matrix(n,n);
for(int i=0;i<n;i++){
for(int j=i;j<n;j++){
A[i,j]=(rnd.NextDouble());
A[j,i]=A[i,j];
}
}
matrix Alow=A.copy();
matrix Ahigh=A.copy();
matrix Vlow=V.copy();
matrix Vhigh=V.copy();
vector elow=e.copy();
vector ehigh=e.copy();
//for plotting:
// matrix B=A.copy();
var sweeps=jacobi.cyclic(A,e,V);
// matrix VTAV = V.T*B*V;
// VTAV.printfloat("Should be diagional:");
var lowest=jacobi.lowest(Alow,elow,1,Vlow); //only the one lowest eigenvalue
// matrix VTAVlow = Vlow.T*B*Vlow;
// VTAVlow.printfloat("Should be diagional:");
var highest=jacobi.highest(Ahigh,ehigh,1,Vhigh); //only the one highest eigenvalue
// matrix VTAVhigh = Vhigh.T*B*Vhigh;
// VTAVhigh.printfloat("Should be diagional:");
WriteLine($"{n} {sweeps} {lowest} {highest}");
}
}//Main
public static void Main()
{
//now test highest EV
int n = 5;
matrix A = new matrix(n, n);
matrix V = new matrix(n, n);
vector e = new vector(n);
var rand = new Random(1);
for (int i = 0; i < n; i++)
{
for (int j = i; j < n; j++)
{
A[i, j] = 2 * (rand.NextDouble() - 0.5);
A[j, i] = A[i, j];
}
}
//2 highest EV
matrix AA2 = A.copy();
string order = "high";
int count = jacobiB.jacobirow(AA2, e, 2, V, order);
e.print("First 2 values should be highest EVs");
//full diag
matrix Vh = new matrix(n, n);
vector eh = new vector(n);
matrix AAh = A.copy();
int counth = jacobiB.jacobirow(AAh, eh, n, Vh);
eh.print("all eigenvalues");
}
public static void Main()
{
WriteLine();
WriteLine("Create a matrix and diagonalize it via the cyclic, value-by-value and" +
" classic method.");
var rand = new Random();
int n = 5;
// Create a random matrix
matrix A = new matrix(n, n);
for (int i = 0; i < n; i++)
{
A[i, i] = 2 - 4 * rand.NextDouble();
for (int j = i + 1; j < n; j++)
{
A[i, j] = 2 - 3.32 * rand.NextDouble();
A[j, i] = A[i, j];
}
}
// Print the random matrix.
WriteLine("\nSetting up a random symmetric matrix:");
A.print();
WriteLine();
// Diagonalize it via the three different methods
matrix Acopy = A.copy();
matrix AcopyII = A.copy();
matrix V = new matrix(n, n);
vector e = new vector(n);
vector eRot = new vector(n);
vector eClassic = new vector(n);
int sweeps = jacobi.cycle(A, e, V);
int entries = (n * n - n) / 2;
int rotations = jacobi.findEigenvalue(Acopy, eRot, V, n, true);
int rotationsClassic = jacobi.classic(AcopyII, eClassic, V);
WriteLine("The cyclic method used {0} operations", sweeps * entries);
WriteLine("The value-by-value method used {0} operations", rotations);
WriteLine("The classic method used {0} operations", rotationsClassic);
WriteLine("\nAfter cyclic diagonalization the matrix looks like:");
A.print();
WriteLine("\nAfter value-by-value diagonalization the matrix looks like:");
Acopy.print();
WriteLine("\nAfter classic Jacobi diagonalization the matrix looks like:");
AcopyII.print();
Write("\n\n");
WriteLine("The found eigenvalues are:");
WriteLine("E(cyclic)\t\tE(val_by_val)\t\tE(classic)");
for (int j = 0; j < n; j++)
{
WriteLine("{0}\t{1}\t{2}", e[j], eRot[j], eClassic[j]);
}
}
static void B4()
{
WriteLine("\n Problem B4\n");
WriteLine("See PlotB4r.svg and PlotB4t.svg for results");
int l = 50; // max matrix size
int k = 5; // number of tries for each avg
Stopwatch sw = new Stopwatch();
var timewriter = new System.IO.StreamWriter("out.b4time.txt");
var rotwriter = new System.IO.StreamWriter("out.b4rot.txt");
vector tvbv = new vector(l);
vector tcyc = new vector(l);
vector rvbv = new vector(l);
vector rcyc = new vector(l);
for (int i = 0; i < l; i++)
{
tvbv[i] = 0;
tcyc[i] = 0;
rvbv[i] = 0;
rcyc[i] = 0;
for (int j = 0; j < k; j++)
{
matrix A = rand_sym_mat(i + 1);
sw.Reset();
matrix B = A.copy();
bool vbv = true;
int evalnum = l;
sw.Start();
jcbi_cycl test_vbv = new jcbi_cycl(B, vbv, evalnum);
sw.Stop();
tvbv[i] += sw.Elapsed.TotalMilliseconds;
rvbv[i] += test_vbv.Rotations;
matrix C = A.copy();
sw.Reset();
sw.Start();
jcbi_cycl test_cyc = new jcbi_cycl(C);
sw.Stop();
tcyc[i] += sw.Elapsed.TotalMilliseconds;
rcyc[i] += test_cyc.Rotations;
}
tvbv[i] = tvbv[i] / k;
tcyc[i] = tcyc[i] / k;
rvbv[i] = rvbv[i] / k;
rcyc[i] = rcyc[i] / k;
timewriter.WriteLine($"{i} {tvbv[i]} {tcyc[i]}");
rotwriter.WriteLine($"{i} {rvbv[i]} {rcyc[i]}");
}
timewriter.Close();
rotwriter.Close();
}
public static void Main(string[] args)
{
int n = 5; //size of real symmetric matrix
matrix A = new matrix(n, n);
var rnd = new Random(1);
for (int i = 0; i < n; i++) /* Construction of a random vector */
{
for (int j = 0; j < n; j++)
{
A[i, j] = (rnd.NextDouble() * 10);
A[j, i] = A[i, j]; /* We make sure that the matrix is symmetric */
}
}
A.print("Random symmetric matrix, A:");
matrix V = new matrix(n, n);
vector e = new vector(n);
matrix B = A.copy(); /* We make a copy of A */
int sweeps = jacobi.cyclic(A, e, V); /* We use the jacobi matlib for the implementation of the cyclic sweeps: public static int cyclic(matrix A, vector e, matrix V=null){...} */
WriteLine($"\nNumber of sweeps: {sweeps}");
matrix D = (V.T * B * V);
// D.print("\nEigenvalue-decomposition, V^T*A*V (should be diagonal):");
D.printfloat("\nEigenvalue-decomposition, V^T*A*V (should be diagonal):"); /* using my print2 to get 0's instead of values with e.g. "e-16"*/
e.print("\nEigenvalues:\n");
}
public givens(matrix A)
{
R = A.copy();
n = A.size1;
m = A.size2;
// Loop over the columns of the matrix
for (int q = 0; q < m; q++)
{
// Loop over the rows of the matrix that lie under the diagonal
for (int p = q + 1; p < n; p++)
{
double theta = Atan2(R[p, q], R[q, q]);
// Loop to recalculate the relevant entries in the matrix
// This runs over all columns in the two rows p and q, which are
// the only ones affected.
for (int k = q; k < m; k++)
{
double xq = R[q, k];
double xp = R[p, k];
R[q, k] = xq * Cos(theta) + xp * Sin(theta);
R[p, k] = -xq *Sin(theta) + xp * Cos(theta);
}
// Store the angle that made R[p,q] = 0 in the spot instead of zero
R[p, q] = theta;
}
}
}
static void Main()
{
WriteLine("Exam problem 21: \nTwo-sided Jacobi algorithm for Singular Value Decomposition (SVD) \n");;
int n = 7;
WriteLine($"Generating Random {n}x{n} matrix");
matrix A = randomMatrix(n, n), U = new matrix(n, n), V = new matrix(n, n);
matrix D = A.copy();
A.print("Matrix A generated:");
WriteLine("Making composition...");
int sweeps = twoSidedJacobiSVD(D, U, V);
WriteLine($"Procedure done!\nNo. of sweeps: {sweeps}.\nNow A --> U*D*V^T, where");
D.print("D: ");
U.print("U: ");
V.print("V: ");
matrix R = (U * D) * V.transpose();
R.print("Test that UDV^T = A");
A.print("A, for reference");
matrix UUt = U * U.transpose(), VVt = V * V.transpose();
UUt.print("Test U*U^T = 1");
VVt.print("Test V*V^T = 1");
}
public qr_decomp_GS(matrix A)
{
int n = A.size1;
int m = A.size2;
q = A.copy();
r = new matrix(m, m);
vector qi = new vector(n);
vector qj = new vector(n);
for (int i = 0; i < m; i++)
{
qi = q.col_toVector(i);
r[i, i] = Sqrt(qi.dot(qi));
for (int k = 0; k < n; k++)
{
q[k, i] = q[k, i] / r[i, i];
}
for (int j = i + 1; j < m; j++)
{
qi = q.col_toVector(i);
qj = q.col_toVector(j);
r[i, j] = qi.dot(qj);
for (int k = 0; k < n; k++)
{
q[k, j] = q[k, j] - q[k, i] * r[i, j];
}
}
}
}
public static int Main()
{
Random rand = new Random();
// Creating the inverse of a matrix
Write("Inverse of matrix\n");
matrix A = new matrix(10, 10);
for (int i = 0; i < A.size1; i++)
{
for (int j = 0; j < A.size2; j++)
{
A[i, j] = rand.NextDouble() * 9.99;
if (A[i, j] > 5.0)
{
A[i, j] *= -1;
}
}
}
Write("A=\n");
print2(A);
matrix Q = A.copy(); matrix R = new matrix(A.size1, A.size2);
matrix.qr_gs_decomp(Q, R);
matrix B = qr_gs_inverse(Q, R);
Write("\nA*B=\n");
print2(A * B);
return(0);
}
static void Main()
{
/// Test on random matrix ///
WriteLine("/////////////// Part A - Test on random matrix ///////////////");
int n = 5;
var rand = new Random(1);
matrix A = new matrix(n, n);
vector e = new vector(n);
matrix V = new matrix(n, n);
for (int i = 0; i < n; i++)
{
for (int j = i; j < n; j++)
{
A[i, j] = rand.NextDouble();
A[j, i] = A[i, j];
}
}
A.print($"random {n}x{n} matrix"); WriteLine();
matrix B = A.copy();
int sweeps = jacobi.cyclic(B, e, V);
WriteLine($"Number of sweeps in jacobi={sweeps}"); WriteLine();
matrix D = (V.T * A * V);
(D).print("Should be a diagonal matrix V.T*B*V="); WriteLine();
e.print("Eigenvalues should equal the diagonal elements above");
vector D_diagonal = new vector(n);
for (int i = 0; i < n; i++)
{
D_diagonal[i] = D[i, i];
}
if (D_diagonal.approx(e))
{
WriteLine("Eigenvalues agree \tTest passed");
}
else
{
WriteLine("Test failed");
}
WriteLine();
matrix A2 = (V * D * V.T);
(A2).print("Check that V*D*V.T=A");
if (A2.approx(A))
{
WriteLine("V*D*V.T = A \tTest passed");
}
else
{
WriteLine("V*D*V.T != A \tTest failed");
}
}
//constructor
public gi_rot(matrix A1)
{
matrix a = A1.copy();
int row = a.size1;
int col = a.size2;
for (int n = 0; n < col; n++)
{
for (int n1 = n + 1; n1 < row; n1++)
{
double the = Atan2(a[n1, n], a[n, n]);
//a[n]=a[n1]*Cos(the)+a[n]*Sin(the);
//a[n1]=(-1)*a[n1]*Sin(the)+a[n]*Cos(the);
//a[n, n1]=the;
for (int n2 = n; n2 < col; n2++)
{
double an_n2 = a[n, n2];
double an1_n2 = a[n1, n2];
a[n, n2] = an_n2 * Cos(the) + an1_n2 * Sin(the);
a[n1, n2] = (-1) * an_n2 * Sin(the) + an1_n2 * Cos(the);
} //end for
a[n1, n] = the;
} //end for
} //end for
A = a;
} //end constructor...
public CholeskyDecomposition(matrix B)
{
matrix A = B.copy();
int size = A.cols;
L = new matrix(size, size);
for (int i = 0; i < size; i++)
{
for (int k = 0; k < (i + 1); k++)
{
double sum = 0;
for (int j = 0; j < k; j++)
{
sum += L[i, j] * L[k, j];
}
if (i == k)
{
// Do this to prevent NaN and +-Infinity
if (A[i, i] - sum <= 0)
{
throw new CholeskyException("Bad Cholesky");
}
L[i, k] = Math.Sqrt(A[i, i] - sum);
}
else
{
L[i, k] = 1 / L[k, k] * (A[i, k] - sum);
}
}
}
}
// Comparison of time and rotations needed to calculate the lowest
// eigenvalue for the cyclic and single value method.
static void probB5()
{
Write("\n\nProblem B.5:\n");
int n = 7;
matrix A1 = matrixHelp.makeRandSymMatrix(n);
matrix A2 = A1.copy();
Write("Finding the largest eigenvalue of the matrix:\n");
A1.print();
matrix V1 = new matrix(n, n);
matrix V2 = new matrix(n, n);
vector e1 = new vector(n);
vector e2 = new vector(n);
int rot1 = jacobi.cyclic(A1, e1, V1);
int rot2 = jacobi.values(A2, e2, 1, V2, true);
Write("\n");
Write($"Largest found by cyclic jacobi: {e1[n-1]}\n");
Write($"Largest found by single row jacobi: {e2[0]}\n");
Write("\nThe single row method was changed by changing the calculation of the rotation angle theta to: 0.5*Atan2(-Apq, App-Aqq)\n");
}
public static void Main()
{
int n = 3;
matrix A = new matrix(n, n);
matrix V = new matrix(n, n);
vector e = new vector(n);
var rand = new Random();
for (int i = 0; i < n; i++)
{
for (int j = i; j < n; j++)
{
A[i, j] = rand.NextDouble() * 10;
A[j, i] = A[i, j];
}
}
A.print("Symmetric matrix A:");
matrix Ao = new matrix(n, n);
Ao = A.copy();
int count = jacobi.cycsweep(A, e, V);
WriteLine($"Number of sweeps: {count}");
V.print("Eigenvectors");
A.print("new A");
e.print("Eigenvalues");
(V.T * Ao * V).print("V^T*A*V diagonal, test");
}
}//constructor
public diagJacobi(matrix x, int firstN, bool eigVec = false, bool largestFirst = false)
{
a = x.copy();
if (largestFirst)
{
a = -1 * a;
}
if (eigVec)
{
v = new matrix(a.size1, a.size1);
v.set_identity();
}
l = new vector(a.size1);
for (int i = 0; i < a.size1; i++)
{
l[i] = a[i, i];
}
bool changed;
sweeps = 0;
rotations = 0;
for (int r = 0; r < firstN; r++)
{
do
{
changed = this.rowSweep(r);
sweeps++;
} while(changed);
}
if (largestFirst)
{
a = -1 * a;
l = -1 * l;
}
}//constructor
public static void Main()
{
// First part of A
int n = 5; // Size of quadratic matrix M.
var rnd = new Random(1);
Console.WriteLine("Part A1");
matrix M = new matrix(n, n);
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
M[i, j] = 4 * rnd.NextDouble() - 1.5;
M[j, i] = M[i, j];
}
}
matrix M2 = M.copy();
M.print("We choose an arbitrary matrix M = ");
vector D = new vector(n);
matrix V = new matrix(n, n);
int sweeps = Jacobi_diagonalization.cyclic_sweep(M, V, D);
matrix temp2 = V.T * M2 * V;
Console.WriteLine($"Total number of sweeps done is = {sweeps}");
temp2.print("V.T*M*V = ");
D.print("Eigenvalues = ");
}
/*
* Remember: a = v^Tav=d;
*/
public diagJacobi(matrix x, bool eigVec = false, bool classic = false)
{
if (classic)
{
this.classicJacobi(x);
}
else
{
a = x.copy();
if (eigVec)
{
v = new matrix(a.size1, a.size1);
v.set_identity();
}
l = new vector(a.size1);
for (int i = 0; i < a.size1; i++)
{
l[i] = a[i, i];
}
bool changed;
sweeps = 0;
rotations = 0;
do
{
changed = this.cyclicSweep();
sweeps++;
} while(changed);
}
}//constructor
static void Main()
{
var rnd = new Random();
var A = new matrix(3, 3);
for (int i = 0; i < A.size1; i++)
{
for (int j = 0; j < A.size2; j++)
{
A[i, j] = 10 * (rnd.NextDouble() - 0.5);
}
}
var Q = A.copy();
var R = new matrix(Q.size2, Q.size2);
WriteLine("\n__________________________________________________________________________________________________________");
WriteLine("Question B:\nMatrix inverse by Gram-Schmidt QR factorization");
WriteLine("Random matrix");
A.print("A = ");
qr_gs.qr_gs_decomp(Q, R);
WriteLine("Factorized into");
Q.print("Q = ");
R.print("R = ");
var B = qr_gs.qr_gs_inverse(Q, R);
WriteLine("Inverse of A:");
B.print("A^-1=B=");
WriteLine("Product should give identity");
(A * B).print("I=AB=");
WriteLine("__________________________________________________________________________________________________________\n");
}
public QRdecompositionGS(matrix A)
{
Q = A.copy();
R = new matrix(Q.size2, Q.size2);
GramSchmidt(A);
}
public static matrix Givens(matrix A)
{
int n = A.size1; int m = A.size2;
matrix A_t = A.copy();
// The Jacobi matrix G is given as an identity matrix except that it contains elements of a rotation matrix
// The elements G_pp, G_qq are replaced with cos(θ) and G_qp and G_pq with -sin(θ) and sin(θ) respectively
// G is therefore a rotation of a vector in the p,q plane
// We aim to use this transformation to zero elements of A by using the Givens rotation: A->GA=A_t
// We must choose θ=Atan2(A[p,q],A[q,q]) to zero the p,qth element in A under a Givens rotation:
for (int q = 0; q < m; q = q + 1)
{
for (int p = q + 1; p < n; p = p + 1)
{
double θ = Atan2(A_t[p, q], A_t[q, q]);
for (int k = q; k < m; k = k + 1)
{
double xq = A_t[q, k]; double xp = A_t[p, k];
A_t[q, k] = xq * Cos(θ) + xp * Sin(θ); A_t[p, k] = -xq *Sin(θ) + xp * Cos(θ);
} // New elements in A, after the rotation
A_t[p, q] = θ;
} // Adding the θ which zero'ed the p,qth entry in A
} // Yields upper triangular matrix R, but with the added θ's which zeroed the corresponding lower triangular entries in A
return(A_t);
}
public static (matrix, int) values(matrix A, int nvalues = 1)
{
bool changed; int rotations = 0, n = A.size1;
matrix D = A.copy();
// matrix V = new matrix(n,n);
// for(int i=0;i<n;i++)V[i,i]=1.0;
for (int p = 0; p < nvalues; p++)
{
do
{
changed = false;
for (int q = p + 1; q < n; q++)
{
double dpp = D[p, p];
double dqq = D[q, q];
double dpq = D[p, q];
double phi = 0.5 * Math.Atan2(2 * dpq, dqq - dpp);
double c = Math.Cos(phi), s = Math.Sin(phi);
double dpp1 = c * c * dpp - 2 * s * c * dpq + s * s * dqq;
double dqq1 = s * s * dpp + 2 * s * c * dpq + c * c * dqq;
if (dpp1 != dpp || dqq1 != dqq)
{
rotations++;
changed = true;
D[p, p] = dpp1;
D[q, q] = dqq1;
D[p, q] = 0.0;
D[q, p] = 0.0;
for (int i = p + 1; i < q; i++)
{
double dpi = D[p, i];
double diq = D[i, q];
D[p, i] = c * dpi - s * diq;
D[i, q] = c * diq + s * dpi;
D[i, p] = D[p, i];
D[q, i] = D[i, q];
}
for (int i = q + 1; i < n; i++)
{
double dpi = D[p, i];
double dqi = D[q, i];
D[p, i] = c * dpi - s * dqi;
D[q, i] = c * dqi + s * dpi;
D[i, p] = D[p, i];
D[i, q] = D[q, i];
}
// for(int i=0;i<n;i++){
// double vip=V[i,p];
// double viq=V[i,q];
// V[i,p]=c*vip-s*viq;
// V[i,q]=c*viq+s*vip;
// }
}
}
}while(changed);
}
return(D, rotations);
}
public gram_s(matrix A)
{
matrix a = A.copy();
int rows_A = A.size1;
int columns_A = A.size2;
matrix q = new matrix(rows_A, columns_A);
if (rows_A < columns_A)
{
Console.Error.WriteLine("Error!!! Matrix A has more columns then rows, try transposing A |\n Error!! \n Error!!!");
}//end if
else
{
matrix R1 = new matrix(columns_A, columns_A);
//somthing else...
for (int n = 0; n < columns_A; n++)
{
R1[n, n] = Sqrt(col_ip(a, n, a, n));
a[n] = a[n] / R1[n, n];
//for(int n1=0; n1<rows_A; n1++){
//a[n1,n]=a[n1,n]/R1[n,n];
//}//end for
for (int n2 = n + 1; n2 < columns_A; n2++)
{
R1[n, n2] = col_ip(a, n, a, n2);
for (int n3 = 0; n3 < rows_A; n3++)
{
a[n3, n2] = a[n3, n2] - a[n3, n] * R1[n, n2];
} //end for
} //end for
} //end for
R = R1;
Q = a;
}//end else
// this does not work... I will try somthing else ...
//for(int n=0; n<columns_A; n++){
//double rnn= Sqrt(col_ip(a,n,a,n)) ;
//R1[n,n]=Sqrt(rnn);
// for(int n1=0; n1<rows_A; n1++){
// q[n1,n]=a[n1,n]/rnn;
// }// end for
// for(int j=n+1; j<columns_A; j++){
// R1[n,j]=col_ip(q,n,a,j);
// for(int n2=0; n2< rows_A; n2++){
// a[n2,j]=a[n2,j]-R1[n,j]*q[n2,n];
// }//end for
// }//end for
//}//end for
//R=R1;
//Q=q;
//}//end else
} // end constructor
static void Main()
{
int n = 5; //size of real symmetric matrix
matrix A = new matrix(n, n);
var rnd = new Random(1);
for (int i = 0; i < n; i++) /* Construction of a random vector */
{
for (int j = 0; j < n; j++)
{
A[i, j] = (rnd.NextDouble() * 10);
A[j, i] = A[i, j]; /* We make sure that the matrix is symmetric */
}
}
A.printfloat("Random symmetric matrix, A:");
matrix V = new matrix(n, n);
matrix V2 = new matrix(n, n);
vector e = new vector(n);
vector e2 = new vector(n);
// matrix B=A.copy();
matrix B2 = A.copy();
int rotations = jacobi.classic2(A, e, V); /* We use the jacobi matlib for the implementation of the cyclic sweeps: public static int cyclic(matrix A, vector e, matrix V=null){...} */
WriteLine($"\nNumber of rotations: {rotations}");
// matrix D=(V.T*B*V);
// D.printfloat("\nClassic: Eigenvalue-decomposition, V^T*A*V (should be diagonal):");
int sweeps = jacobi.cyclic(B2, e2, V2);
WriteLine($"Number of sweeps: {sweeps}");
// matrix D2=(V2.T*B*V2);
// D2.printfloat("\nCyclic: Eigenvalue-decomposition, V^T*A*V (should be diagonal):");
// var rnd = new Random(1);
// int n = 4;
// matrix v_c = new matrix(n,n);
// matrix v_classic = new matrix(n,n);
// vector e_classic = new vector(n);
// var A_c = new matrix(n,n);
// for(int i=0;i<n;i++){
// for(int j=i;j<n;j++){
// A_c[i,j]=2*(rnd.NextDouble()-0.5);
// A_c[j,i]=A_c[i,j];
// }
// }
// matrix B=A_c.copy();
// var A_classic = A_c.copy();
// int rotations = classic(A_classic,e_classic,v_classic);
// A_classic.printfloat("A classic transformed");
// // e_classic.print("Eigenvalues calculated with classic:");
// // v_classic.print("Eigenvectors calculated with classic:");
// var VTAV=v_classic.T*B*v_classic;
// VTAV.printfloat("V^T*A*V:");
}
public static vector high_eig(matrix A, int nvalues = 1)
{
bool changed; int n = A.size1;
matrix D = A.copy();
for (int p = 0; p < nvalues; p++)
{
do
{
changed = false;
for (int q = p + 1; q < n; q++)
{
double dpp = D[p, p];
double dqq = D[q, q];
double dpq = D[p, q];
double phi = 0.5 * Math.Atan2(-2 * dpq, -dqq + dpp);
double c = Math.Cos(phi), s = Math.Sin(phi);
double dpp1 = c * c * dpp - 2 * s * c * dpq + s * s * dqq;
double dqq1 = s * s * dpp + 2 * s * c * dpq + c * c * dqq;
if (dpp1 != dpp || dqq1 != dqq)
{
changed = true;
D[p, p] = dpp1;
D[q, q] = dqq1;
D[p, q] = 0.0;
D[q, p] = 0.0;
for (int i = p + 1; i < q; i++)
{
double dpi = D[p, i];
double diq = D[i, q];
D[p, i] = c * dpi - s * diq;
D[i, q] = c * diq + s * dpi;
D[i, p] = D[p, i];
D[q, i] = D[i, q];
}
for (int i = q + 1; i < n; i++)
{
double dpi = D[p, i];
double dqi = D[q, i];
D[p, i] = c * dpi - s * dqi;
D[q, i] = c * dqi + s * dpi;
D[i, p] = D[p, i];
D[i, q] = D[q, i];
}
}
}
}while(changed);
}
vector e = new vector(n);
for (int i = 0; i < n; i++)
{
e[i] = D[i, i];
}
return(e);
}
public static void Main()
{
for (int n = 5; n < 300; n = n + 50)
{
matrix A = new matrix(n, n);
matrix V = new matrix(n, n);
vector e = new vector(n);
var rand = new Random(1);
for (int i = 0; i < n; i++)
{
for (int j = i; j < n; j++)
{
A[i, j] = 2 * (rand.NextDouble() - 0.5);
A[j, i] = A[i, j];
}
}
Stopwatch rowt = new Stopwatch(); //one EV
matrix AAf = A.copy();
rowt.Start();
int count = jacobiB.jacobirow(AAf, e, 1, V);
rowt.Stop();
Stopwatch rowh = new Stopwatch(); //full diag
matrix Vh = new matrix(n, n);
vector eh = new vector(n);
matrix AAh = A.copy();
rowh.Start();
int counth = jacobiB.jacobirow(AAh, eh, n, Vh);
rowh.Stop();
matrix Vt = new matrix(n, n);//cyclic
vector et = new vector(n);
matrix AAt = A.copy();
Stopwatch cyct = new Stopwatch();
cyct.Start();
int countt = jacobi.cycsweep(AAt, et, Vt);
cyct.Stop();
WriteLine($"{n} {rowt.ElapsedMilliseconds} {count} {rowh.ElapsedMilliseconds} {counth} {cyct.ElapsedMilliseconds} {countt}");
}
}
static void Main()
{
// Testing that the jacobiAlgorithm works:
int n = 5;
vector e = new vector(n);
matrix V = new matrix(n, n);
matrix A = generateRandommatrix(n, n);
matrix B = A.copy();
int sweeps = jacobi.jacobi_cyclic(A, e, V);
WriteLine("---------Problem A1----------");
WriteLine("\nTesting that the Jacobi algorithm works as intended:");
A.print("\nRandom square matrix A:");
WriteLine("\nEigenvalue decomposition of A=V*D*V^T:");
(V.transpose() * B * V).print("\nD = V^T*A*V = ");
e.print("\nListed eigenvalues:\t");
WriteLine($"\nNumber of sweeps required to diagonalize A: {sweeps}");
// Particle in a box
n = 50;
matrix H = Hamiltonian.boxHamilton(n);
B = H.copy();
V = new matrix(n, n);
e = new vector(n);
sweeps = jacobi.jacobi_cyclic(H, e, V);
WriteLine("\n---------Problem A2----------");
WriteLine("\nNow solving for a particle in a box.");
WriteLine($"\nThe dimensions of the soon to be diagonalized Hamiltonian is {n}x{n}");
WriteLine("\nComparison of the calculated eigenvalues and the exact:\n");
WriteLine("n Calculated Exact:");
for (int k = 0; k < n / 3; k++)
{
double exact = PI * PI * (k + 1) * (k + 1);
double calculated = e[k];
WriteLine($"{k} {calculated:f4}\t {exact:f4}");
}
// plotting time
StreamWriter solutions = new StreamWriter("SolutionsA.txt");
for (int i = 0; i < n; i++)
{
solutions.Write($"{i*(1.0/n)}");
for (int k = 0; k < 5; k++)
{
solutions.Write($" {V[i,k]}");
}
solutions.Write("\n");
}
solutions.Close();
}
static void Main()
{
/////////////// QM-problem ///////////////
WriteLine($"/////////////// Part A - QM-problem ///////////////");
int N = 100;
double s = 1.0 / (N + 1);
matrix H = new matrix(N, N);
for (int i = 0; i < N - 1; i++)
{
H[i, i] = -2;
H[i, i + 1] = 1;
H[i + 1, i] = 1;
}
H[N - 1, N - 1] = -2;
H = -H / s / s;
matrix H1 = H.copy();
vector eigenvals = new vector(N);
matrix V = new matrix(N, N);
int sweeps = jacobi.cyclic(H1, eigenvals, V);
// H.print($"Hamilton {H.size1}x{H.size2} matrix"); WriteLine();
WriteLine($"Number of sweeps in jacobi={sweeps}"); WriteLine();
// matrix D=(V.T*H*V);
// (D).print("Should be a diagonal matrix V.T*H*V=");WriteLine();
// eigenvals.print("Eigenvalues should equal the diagonal elements above"); WriteLine();
// matrix H2=(V*D*V.T);
// (H2).print("Check that V*D*V.T=H"); WriteLine();
WriteLine($"k \t Calculated \t Exact \t Relative deviation");
for (int k = 0; k < N / 3; k++)
{
double exact = PI * PI * (k + 1) * (k + 1);
double calculated = eigenvals[k];
WriteLine($"{k} \t {calculated} \t {exact} \t {(calculated-exact)/exact*100}%");
}
for (int k = 0; k < 3; k++)
{
Error.WriteLine($"{0} {0}");
for (int i = 0; i < N; i++)
{
Error.WriteLine($"{(i+1.0)/(N+1)} {V[i,k]}");
}
Error.WriteLine($"{1} {0}");
Error.WriteLine(); Error.WriteLine();
}
}
public static void Main()
{
// Setup the output file:
var outfile = new System.IO.StreamWriter("data.convergence_size.txt");
int mMax = 30;
int[] ns = new int[] { 5, 10, 30, 50, 100, 150, 250 };
foreach (int n in ns)
{
matrix A = matrixHelp.makeRandSymMatrix(n);
// Using m = n ensures that all the right eigenvalues are found
matrix E_full = new matrix(n, n);
vector e_full = new vector(n);
lanczos.eigenvalues(A, E_full, e_full);
for (int m = 1; (m < n) && (m <= mMax); m++)
{
matrix A_m = A.copy();
matrix E_m = new matrix(n, m);
vector e_m = new vector(m);
lanczos.eigenvalues(A_m, E_m, e_m);
Func <int, double> e = (i) => {
if (i < m)
{
return(Abs(e_m[(e_m.size - 1) - i] - e_full[e_full.size - 1 - i]));
}
else
{
return(Double.NaN);
}
};
Func <int, double> E_div = (i) => { if (i < m)
{
double norm1 = (E_m[E_m.size2 - 1 - i] - E_full[E_full.size2 - 1 - i]).norm();
double norm2 = (E_m[E_m.size2 - 1 - i] + E_full[E_full.size2 - 1 - i]).norm();
return((norm1 < norm2)?norm1:norm2);
}
else
{
return(Double.NaN);
} };
outfile.Write("{0} {1} {2}\n", m, e(0), E_div(0));
}
outfile.Write("\n\n");
}
outfile.Close();
}
