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eigenize dogleg()
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@ -1,8 +1,11 @@
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template <typename Scalar>
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void ei_dogleg(int n, const Scalar *r__, int /* lr*/ ,
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const Scalar *diag, const Scalar *qtb, Scalar delta, Scalar *x,
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Scalar *wa1, Scalar *wa2)
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template <typename Scalar>
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void ei_dogleg(
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Matrix< Scalar, Dynamic, 1 > &r__,
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const Matrix< Scalar, Dynamic, 1 > &diag,
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const Matrix< Scalar, Dynamic, 1 > &qtb,
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Scalar delta,
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Matrix< Scalar, Dynamic, 1 > &x)
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{
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/* Local variables */
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int i, j, k, l, jj, jp1;
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@ -10,30 +13,26 @@ void ei_dogleg(int n, const Scalar *r__, int /* lr*/ ,
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Scalar gnorm, qnorm;
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Scalar sgnorm;
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/* Parameter adjustments */
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--wa2;
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--wa1;
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--x;
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--qtb;
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--diag;
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--r__;
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/* Function Body */
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const Scalar epsmch = epsilon<Scalar>();
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const int n = diag.size();
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Matrix< Scalar, Dynamic, 1 > wa1(n), wa2(n);
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assert(n==qtb.size());
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assert(n==x.size());
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/* first, calculate the gauss-newton direction. */
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jj = n * (n + 1) / 2 + 1;
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for (k = 1; k <= n; ++k) {
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j = n - k + 1;
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jj = n * (n + 1) / 2;
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for (k = 0; k < n; ++k) {
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j = n - k - 1;
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jp1 = j + 1;
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jj -= k;
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jj -= k+1;
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l = jj + 1;
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sum = 0.;
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if (n < jp1) {
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goto L20;
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}
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for (i = jp1; i <= n; ++i) {
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for (i = jp1; i < n; ++i) {
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sum += r__[l] * x[i];
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++l;
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/* L10: */
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@ -44,7 +43,7 @@ L20:
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goto L40;
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}
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l = j;
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for (i = 1; i <= j; ++i) {
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for (i = 0; i <= j; ++i) {
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/* Computing MAX */
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temp = std::max(temp,ei_abs(r__[l]));
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l = l + n - i;
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@ -61,12 +60,12 @@ L40:
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/* test whether the gauss-newton direction is acceptable. */
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for (j = 1; j <= n; ++j) {
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for (j = 0; j < n; ++j) {
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wa1[j] = 0.;
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wa2[j] = diag[j] * x[j];
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/* L60: */
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}
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qnorm = Map< Matrix< Scalar, Dynamic, 1 > >(&wa2[1],n).stableNorm();
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qnorm = wa2.stableNorm();
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if (qnorm <= delta) {
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/* goto L140; */
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return;
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@ -75,10 +74,10 @@ L40:
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/* the gauss-newton direction is not acceptable. */
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/* next, calculate the scaled gradient direction. */
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l = 1;
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for (j = 1; j <= n; ++j) {
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l = 0;
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for (j = 0; j < n; ++j) {
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temp = qtb[j];
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for (i = j; i <= n; ++i) {
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for (i = j; i < n; ++i) {
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wa1[i] += r__[l] * temp;
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++l;
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/* L70: */
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@ -90,7 +89,7 @@ L40:
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/* calculate the norm of the scaled gradient and test for */
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/* the special case in which the scaled gradient is zero. */
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gnorm = Map< Matrix< Scalar, Dynamic, 1 > >(&wa1[1],n).stableNorm();
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gnorm = wa1.stableNorm();
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sgnorm = 0.;
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alpha = delta / qnorm;
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if (gnorm == 0.) {
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@ -100,14 +99,14 @@ L40:
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/* calculate the point along the scaled gradient */
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/* at which the quadratic is minimized. */
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for (j = 1; j <= n; ++j) {
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for (j = 0; j < n; ++j) {
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wa1[j] = wa1[j] / gnorm / diag[j];
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/* L90: */
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}
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l = 1;
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for (j = 1; j <= n; ++j) {
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l = 0;
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for (j = 0; j < n; ++j) {
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sum = 0.;
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for (i = j; i <= n; ++i) {
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for (i = j; i < n; ++i) {
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sum += r__[l] * wa1[i];
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++l;
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/* L100: */
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@ -115,7 +114,7 @@ L40:
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wa2[j] = sum;
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/* L110: */
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}
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temp = Map< Matrix< Scalar, Dynamic, 1 > >(&wa2[1],n).stableNorm();
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temp = wa2.stableNorm();
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sgnorm = gnorm / temp / temp;
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/* test whether the scaled gradient direction is acceptable. */
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@ -129,7 +128,7 @@ L40:
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/* finally, calculate the point along the dogleg */
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/* at which the quadratic is minimized. */
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bnorm = Map< Matrix< Scalar, Dynamic, 1 > >(&qtb[1],n).stableNorm();
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bnorm = qtb.stableNorm();
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temp = bnorm / gnorm * (bnorm / qnorm) * (sgnorm / delta);
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/* Computing 2nd power */
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temp = temp - delta / qnorm * ei_abs2(sgnorm / delta) + ei_sqrt(ei_abs2(temp - delta / qnorm) + (1.-ei_abs2(delta / qnorm)) * (1.-ei_abs2(sgnorm / delta)));
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@ -141,14 +140,10 @@ L120:
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/* direction and the scaled gradient direction. */
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temp = (1. - alpha) * std::min(sgnorm,delta);
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for (j = 1; j <= n; ++j) {
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for (j = 0; j < n; ++j) {
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x[j] = temp * wa1[j] + alpha * x[j];
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/* L130: */
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}
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/* L140: */
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return;
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/* last card of subroutine dogleg. */
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} /* dogleg_ */
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}
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@ -209,7 +209,7 @@ L190:
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/* determine the direction p. */
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ei_dogleg<Scalar>(n, R.data(), lr, diag.data(), qtf.data(), delta, wa1.data(), wa2.data(), wa3.data());
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ei_dogleg<Scalar>(R, diag, qtf, delta, wa1);
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/* store the direction p and x + p. calculate the norm of p. */
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@ -198,7 +198,7 @@ L190:
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/* determine the direction p. */
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ei_dogleg<Scalar>(n, R.data(), lr, diag.data(), qtf.data(), delta, wa1.data(), wa2.data(), wa3.data());
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ei_dogleg<Scalar>(R, diag, qtf, delta, wa1);
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/* store the direction p and x + p. calculate the norm of p. */
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