vecmat3: diagonalization replaced by Jacobi to avoid numerical errors when already close to diagonal
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1ee984eda2
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10
t.cc
10
t.cc
@ -2461,7 +2461,7 @@ Polynomial<double> pp({1,2,3,4,5});
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cout<<pp;
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}
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if(0)
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if(1)
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{
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//prepare random symmetric mat3
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int seed;
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@ -2470,10 +2470,14 @@ if(sizeof(int)!=read(f,&seed,sizeof(int))) laerror("cannot read /dev/random");
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close(f);
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srand(seed);
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double scale;
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cin >> scale;
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NRMat<double> tmp(3,3);
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tmp.randomize(2.);
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tmp.randomize(scale);
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Mat3<double> mm(tmp);
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mm.symmetrize();
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mm+= 1.;
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NRMat<double> m(&mm[0][0],3,3);
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cout <<m<<"3 3\n"<<mm<<endl;
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@ -3149,7 +3153,7 @@ for(int i=0; i<a.size(); ++i)
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}
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}
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if(1)
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if(0)
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{
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}
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162
vecmat3.cc
162
vecmat3.cc
@ -91,17 +91,24 @@ template<typename T>
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void Vec3<T>::inertia(Mat3<T> &m, const T weight) const
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{
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T r2 = q[0]*q[0]+q[1]*q[1]+q[2]*q[2];
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T tmp;
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m[0][0] -= weight*(q[0]*q[0]-r2);
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m[0][1] -= weight*q[0]*q[1];
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m[0][2] -= weight*q[0]*q[2];
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m[1][0] -= weight*q[1]*q[0];
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tmp= weight*q[0]*q[1];
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m[0][1] -= tmp;
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m[1][0] -= tmp;
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tmp = weight*q[0]*q[2];
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m[0][2] -= tmp;
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m[2][0] -= tmp;
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m[1][1] -= weight*(q[1]*q[1]-r2);
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m[1][2] -= weight*q[1]*q[2];
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m[2][0] -= weight*q[2]*q[0];
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m[2][1] -= weight*q[2]*q[1];
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tmp = weight*q[1]*q[2];
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m[1][2] -= tmp;
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m[2][1] -= tmp;
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m[2][2] -= weight*(q[2]*q[2]-r2);
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}
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@ -671,6 +678,14 @@ else for(int i=0;i<3;++i) for(int j=0; j<3; ++j) q[i][j]=x*RANDDOUBLESIGNED();
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#endif
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#if 0
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//the Kopp's method is numerically unstable if the matrix is already close to diagonal
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//for example repeated diagonalization of inertia tensor and performing a body rotation
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//yields nonsense on the second run
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//I switched to Jacobi
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//
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//eigensolver for 3x3 matrix by Joachim Kopp - analytic formula version,
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//might be unstable for ill-conditioned ones, then use other methods
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//cf. arxiv physics 0610206v3
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@ -703,7 +718,7 @@ else for(int i=0;i<3;++i) for(int j=0; j<3; ++j) q[i][j]=x*RANDDOUBLESIGNED();
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#define SQR(x) ((x)*(x)) // x^2
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//
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template <typename T>
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void Mat3<T>::eival_sym(Vec3<T> &w, const bool sortdown) const
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static void eival_sym(const Mat3<T> &q, Vec3<T> &w, const bool sortdown)
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{
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T m, c1, c0;
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@ -721,13 +736,13 @@ T m, c1, c0;
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c0 = q[2][2]*dd + q[0][0]*ee + q[1][1]*ff - q[0][0]*q[1][1]*q[2][2]
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- 2.0 * q[0][2]*de; // c*d^2 + a*e^2 + b*f^2 - a*b*c - 2*f*d*e)
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T p, sqrt_p, q, c, s, phi;
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T p, sqrt_p, Q, c, s, phi;
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p = SQR(m) - 3.0*c1;
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q = m*(p - (3.0/2.0)*c1) - (27.0/2.0)*c0;
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Q = m*(p - (3.0/2.0)*c1) - (27.0/2.0)*c0;
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sqrt_p = sqrt(abs(p));
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phi = 27.0 * ( 0.25*SQR(c1)*(p - c1) + c0*(q + 27.0/4.0*c0));
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phi = (1.0/3.0) * atan2(sqrt(abs(phi)), q);
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phi = 27.0 * ( 0.25*SQR(c1)*(p - c1) + c0*(Q + 27.0/4.0*c0));
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phi = (1.0/3.0) * atan2(sqrt(abs(phi)), Q);
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c = sqrt_p*cos(phi);
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s = (1.0/M_SQRT3)*sqrt_p*sin(phi);
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@ -778,7 +793,7 @@ else
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// v1.0: First released version
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// ----------------------------------------------------------------------------
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template <typename T>
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void Mat3<T>::eivec_sym(Vec3<T> &w, Mat3 &v, const bool sortdown) const
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bool Mat3<T>::eivec_sym(Vec3<T> &w, Mat3 &v, const bool sortdown) const
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{
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T norm; // Squared norm or inverse norm of current eigenvector
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T n0, n1; // Norm of first and second columns of A
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@ -790,13 +805,13 @@ void Mat3<T>::eivec_sym(Vec3<T> &w, Mat3 &v, const bool sortdown) const
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int i, j; // Loop counters
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// Calculate eigenvalues
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eival_sym(w,sortdown);
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eival_sym(*this, w,sortdown);
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Mat3<T> A(*this); //scratch copy
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wmax = fabs(w[0]);
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if ((t=fabs(w[1])) > wmax)
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wmax = abs(w[0]);
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if ((t=abs(w[1])) > wmax)
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wmax = t;
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if ((t=fabs(w[2])) > wmax)
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if ((t=abs(w[2])) > wmax)
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wmax = t;
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thresh = SQR(8.0 * DBL_EPSILON * wmax);
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@ -857,7 +872,7 @@ Mat3<T> A(*this); //scratch copy
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// Prepare calculation of second eigenvector
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t = w[0] - w[1];
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if (fabs(t) > 8.0 * DBL_EPSILON * wmax)
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if (abs(t) > 8.0 * DBL_EPSILON * wmax)
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{
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// For non-degenerate eigenvalue, calculate second eigenvector by the formula
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// v[1] = (A - w[1]).e1 x (A - w[1]).e2
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@ -959,11 +974,122 @@ Mat3<T> A(*this); //scratch copy
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v[1][2] = v[2][0]*v[0][1] - v[0][0]*v[2][1];
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v[2][2] = v[0][0]*v[1][1] - v[1][0]*v[0][1];
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return 0;
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}
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#undef SQR
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//end eigensolver for 3x3 matrix
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//end Kopp's eigensolver for 3x3 matrix
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/////////////////////////////////////////////////////////////////////////////////////////////
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#else
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//begin Jacobi's eigensolver for 3x3 matrix
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#define ROTATE(a,i,j,k,l) g=a[i][j];h=a[k][l];a[i][j]=g-s*(h+g*tau); a[k][l]=h+s*(g-h*tau);
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#define SWAP(i,j) {T tmp; tmp=d[i]; d[i]=d[j]; d[j]=tmp; for(int ii=0; ii<3; ++ii) {tmp=v[ii][i]; v[ii][i]=v[ii][j]; v[ii][j]=tmp;}}
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template <typename T>
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bool Mat3<T>::eivec_sym(Vec3<T> &d, Mat3 &v, const bool sortdown) const
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{
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Mat3<T> a(*this);//will be overwritten but we are const
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const int n=3;
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int j,iq,ip,i;
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T tresh,theta,tau,t,sm,s,h,g,c;
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Vec3<T> b,z;
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for (ip=0;ip<n;ip++) {
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for (iq=0;iq<n;iq++) v[ip][iq]=0.0;
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v[ip][ip]=1.0;
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}
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for (ip=0;ip<n;ip++) {
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b[ip]=d[ip]=a[ip][ip];
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z[ip]=0.0;
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}
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for (i=1;i<=50;i++) {
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sm=0.0;
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for (ip=0;ip<n-1;ip++) {
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for (iq=ip+1;iq<n;iq++)
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sm += abs(a[ip][iq]);
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}
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if (sm == 0.0)
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{
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if(sortdown)
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{
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if(d[0]<d[1]) SWAP(0,1)
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if(d[0]<d[2]) SWAP(0,2)
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if(d[1]<d[2]) SWAP(1,2)
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}
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else
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{
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if(d[0]>d[1]) SWAP(0,1)
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if(d[0]>d[2]) SWAP(0,2)
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if(d[1]>d[2]) SWAP(1,2)
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}
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return 0;
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}
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if (i < 4)
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tresh=0.2*sm/(n*n);
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else
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tresh=0.0;
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for (ip=0;ip<n-1;ip++) {
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for (iq=ip+1;iq<n;iq++) {
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g=100.0*abs(a[ip][iq]);
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if (i > 4 && abs(d[ip])+g == abs(d[ip])
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&& abs(d[iq])+g == abs(d[iq]))
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a[ip][iq]=0.0;
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else if (abs(a[ip][iq]) > tresh) {
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h=d[iq]-d[ip];
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if (abs(h)+g == abs(h))
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t=(a[ip][iq])/h;
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else {
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theta=0.5*h/(a[ip][iq]);
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t=1.0/(abs(theta)+sqrt(1.0+theta*theta));
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if (theta < 0.0) t = -t;
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}
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c=1.0/sqrt(1+t*t);
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s=t*c;
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tau=s/(1.0+c);
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h=t*a[ip][iq];
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z[ip] -= h;
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z[iq] += h;
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d[ip] -= h;
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d[iq] += h;
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a[ip][iq]=0.0;
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for (j=0;j<=ip-1;j++) {
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ROTATE(a,j,ip,j,iq)
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}
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for (j=ip+1;j<=iq-1;j++) {
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ROTATE(a,ip,j,j,iq)
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}
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for (j=iq+1;j<n;j++) {
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ROTATE(a,ip,j,iq,j)
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}
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for (j=0;j<n;j++) {
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ROTATE(v,j,ip,j,iq)
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}
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}
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}
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}
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for (ip=0;ip<n;ip++) {
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b[ip] += z[ip];
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d[ip]=b[ip];
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z[ip]=0.0;
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}
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}
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return 1;
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}
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#undef ROTATE
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#undef SWAP
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#endif
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//end Jacobi's eigensolver for 3x3 matrix
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/////////////////////////////////////////////////////////////////////////////////////////////
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template<typename T>
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T Mat3<T>::norm(const T scalar) const
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@ -183,8 +183,7 @@ public:
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int fprintf(FILE *f, const char *format) const {int n= ::fprintf(f,format,q[0][0],q[0][1],q[0][2]); n+=::fprintf(f,format,q[1][0],q[1][1],q[1][2]); n+=::fprintf(f,format,q[2][0],q[2][1],q[2][2]); return n;};
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int fscanf(FILE *f, const char *format) const {return ::fscanf(f,format,q[0][0],q[0][1],q[0][2]) + ::fscanf(f,format,q[1][0],q[1][1],q[1][2]) + ::fscanf(f,format,q[2][0],q[2][1],q[2][2]);};
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void symmetrize(); //average offdiagonal elements
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void eival_sym(Vec3<T> &w, const bool sortdown=false) const; //only for real symmetric matrix, symmetry is not checked
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void eivec_sym(Vec3<T> &w, Mat3 &v, const bool sortdown=false) const; //only for real symmetric matrix, symmetry is not checked
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bool eivec_sym(Vec3<T> &w, Mat3 &v, const bool sortdown=false) const; //only for real symmetric matrix, symmetry is not checked, returns false on success
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T norm(const T scalar = 0) const;
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void qrd(Mat3 &q, Mat3 &r); //not const, destroys the matrix
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void svd(Mat3 &u, Vec3<T> &w, Mat3 &v, bool proper_rotations=false) const; //if proper_rotations = true, singular value can be negative but u and v are proper rotations
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