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only indentation fixes (this eases porting)
This commit is contained in:
Thomas Capricelli
parent
feb5af3ede
commit
f793dbe45c
@ -1,8 +1,8 @@
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template <typename Scalar>
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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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const Scalar *diag, const Scalar *qtb, Scalar delta, Scalar *x,
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Scalar *wa1, Scalar *wa2)
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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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@ -21,134 +21,134 @@ void ei_dogleg(int n, const Scalar *r__, int /* lr*/ ,
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/* Function Body */
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const Scalar epsmch = epsilon<Scalar>();
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/* first, calculate the gauss-newton direction. */
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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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jp1 = j + 1;
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jj -= k;
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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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sum += r__[l] * x[i];
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++l;
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/* L10: */
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}
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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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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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sum += r__[l] * x[i];
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++l;
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/* L10: */
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}
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L20:
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temp = r__[jj];
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if (temp != 0.) {
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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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/* 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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/* L30: */
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}
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temp = epsmch * temp;
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if (temp == 0.) {
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temp = epsmch;
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}
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temp = r__[jj];
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if (temp != 0.) {
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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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/* 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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/* L30: */
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}
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temp = epsmch * temp;
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if (temp == 0.) {
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temp = epsmch;
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}
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L40:
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x[j] = (qtb[j] - sum) / temp;
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/* L50: */
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x[j] = (qtb[j] - sum) / temp;
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/* L50: */
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}
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/* test whether the gauss-newton direction is acceptable. */
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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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wa1[j] = 0.;
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wa2[j] = diag[j] * x[j];
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/* L60: */
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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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if (qnorm <= delta) {
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/* goto L140; */
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/* goto L140; */
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return;
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}
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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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/* 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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temp = qtb[j];
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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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}
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wa1[j] /= diag[j];
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/* L80: */
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temp = qtb[j];
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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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}
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wa1[j] /= diag[j];
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/* L80: */
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}
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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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/* 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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sgnorm = 0.;
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alpha = delta / qnorm;
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if (gnorm == 0.) {
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goto L120;
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goto L120;
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}
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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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/* 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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wa1[j] = wa1[j] / gnorm / diag[j];
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/* L90: */
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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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sum = 0.;
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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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}
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wa2[j] = sum;
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/* L110: */
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sum = 0.;
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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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}
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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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sgnorm = gnorm / temp / temp;
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/* test whether the scaled gradient direction is acceptable. */
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/* test whether the scaled gradient direction is acceptable. */
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alpha = 0.;
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if (sgnorm >= delta) {
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goto L120;
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goto L120;
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}
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/* the scaled gradient direction is not acceptable. */
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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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/* the scaled gradient direction is not acceptable. */
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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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temp = bnorm / gnorm * (bnorm / qnorm) * (sgnorm / delta);
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/* Computing 2nd power */
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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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/* Computing 2nd power */
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/* Computing 2nd power */
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alpha = delta / qnorm * (1. - ei_abs2(sgnorm / delta)) / temp;
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L120:
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/* form appropriate convex combination of the gauss-newton */
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/* direction and the scaled gradient direction. */
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/* form appropriate convex combination of the gauss-newton */
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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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x[j] = temp * wa1[j] + alpha * x[j];
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/* L130: */
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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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/* L140: */
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return;
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/* last card of subroutine dogleg. */
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/* last card of subroutine dogleg. */
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} /* dogleg_ */
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@ -31,174 +31,174 @@ void ei_lmpar(
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Matrix< Scalar, Dynamic, 1 > wa1(n), wa2(n);
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/* compute and store in x the gauss-newton direction. if the */
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/* jacobian is rank-deficient, obtain a least squares solution. */
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/* compute and store in x the gauss-newton direction. if the */
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/* jacobian is rank-deficient, obtain a least squares solution. */
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nsing = n-1;
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for (j = 0; j < n; ++j) {
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wa1[j] = qtb[j];
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if (r__(j,j) == 0. && nsing == n-1)
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nsing = j - 1;
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if (nsing < n-1)
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wa1[j] = 0.;
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wa1[j] = qtb[j];
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if (r__(j,j) == 0. && nsing == n-1)
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nsing = j - 1;
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if (nsing < n-1)
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wa1[j] = 0.;
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}
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for (k = 0; k <= nsing; ++k) {
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j = nsing - k;
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wa1[j] /= r__(j,j);
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temp = wa1[j];
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jm1 = j - 1;
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for (i = 0; i <= jm1; ++i)
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wa1[i] -= r__(i,j) * temp;
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j = nsing - k;
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wa1[j] /= r__(j,j);
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temp = wa1[j];
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jm1 = j - 1;
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for (i = 0; i <= jm1; ++i)
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wa1[i] -= r__(i,j) * temp;
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}
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for (j = 0; j < n; ++j) {
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l = ipvt[j]-1;
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x[l] = wa1[j];
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l = ipvt[j]-1;
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x[l] = wa1[j];
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}
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/* initialize the iteration counter. */
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/* evaluate the function at the origin, and test */
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/* for acceptance of the gauss-newton direction. */
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/* initialize the iteration counter. */
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/* evaluate the function at the origin, and test */
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/* for acceptance of the gauss-newton direction. */
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iter = 0;
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for (j = 0; j < n; ++j) {
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wa2[j] = diag[j] * x[j];
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/* L70: */
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wa2[j] = diag[j] * x[j];
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/* L70: */
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}
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dxnorm = wa2.blueNorm();
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fp = dxnorm - delta;
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if (fp <= Scalar(0.1) * delta) {
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goto L220;
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goto L220;
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}
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/* if the jacobian is not rank deficient, the newton */
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/* step provides a lower bound, parl, for the zero of */
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/* the function. otherwise set this bound to zero. */
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/* if the jacobian is not rank deficient, the newton */
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/* step provides a lower bound, parl, for the zero of */
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/* the function. otherwise set this bound to zero. */
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parl = 0.;
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if (nsing < n-1) {
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goto L120;
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goto L120;
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}
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for (j = 0; j < n; ++j) {
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l = ipvt[j]-1;
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wa1[j] = diag[l] * (wa2[l] / dxnorm);
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l = ipvt[j]-1;
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wa1[j] = diag[l] * (wa2[l] / dxnorm);
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}
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for (j = 0; j < n; ++j) {
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sum = 0.;
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jm1 = j - 1;
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for (i = 0; i <= jm1; ++i)
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sum += r__(i,j) * wa1[i];
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wa1[j] = (wa1[j] - sum) / r__(j,j);
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sum = 0.;
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jm1 = j - 1;
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for (i = 0; i <= jm1; ++i)
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sum += r__(i,j) * wa1[i];
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wa1[j] = (wa1[j] - sum) / r__(j,j);
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}
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temp = wa1.blueNorm();
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parl = fp / delta / temp / temp;
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L120:
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/* calculate an upper bound, paru, for the zero of the function. */
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/* calculate an upper bound, paru, for the zero of the function. */
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for (j = 0; j < n; ++j) {
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sum = 0.;
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for (i = 0; i <= j; ++i) {
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sum += r__(i,j) * qtb[i];
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/* L130: */
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}
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l = ipvt[j]-1;
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wa1[j] = sum / diag[l];
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/* L140: */
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sum = 0.;
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for (i = 0; i <= j; ++i) {
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sum += r__(i,j) * qtb[i];
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/* L130: */
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}
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l = ipvt[j]-1;
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wa1[j] = sum / diag[l];
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/* L140: */
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}
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gnorm = wa1.stableNorm();
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paru = gnorm / delta;
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if (paru == 0.) {
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paru = dwarf / std::min(delta,Scalar(0.1));
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paru = dwarf / std::min(delta,Scalar(0.1));
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}
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/* if the input par lies outside of the interval (parl,paru), */
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/* set par to the closer endpoint. */
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/* if the input par lies outside of the interval (parl,paru), */
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/* set par to the closer endpoint. */
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par = std::max(par,parl);
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par = std::min(par,paru);
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if (par == 0.) {
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par = gnorm / dxnorm;
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par = gnorm / dxnorm;
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}
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/* beginning of an iteration. */
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/* beginning of an iteration. */
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L150:
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++iter;
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/* evaluate the function at the current value of par. */
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/* evaluate the function at the current value of par. */
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if (par == 0.) {
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/* Computing MAX */
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par = std::max(dwarf,Scalar(.001) * paru);
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/* Computing MAX */
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par = std::max(dwarf,Scalar(.001) * paru);
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}
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temp = ei_sqrt(par);
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for (j = 0; j < n; ++j) {
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wa1[j] = temp * diag[j];
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/* L160: */
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wa1[j] = temp * diag[j];
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/* L160: */
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}
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ei_qrsolv<Scalar>(n, r__.data(), r__.rows(), ipvt.data(), wa1.data(), qtb.data(), x.data(), sdiag.data(), wa2.data());
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for (j = 0; j < n; ++j) {
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wa2[j] = diag[j] * x[j];
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/* L170: */
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wa2[j] = diag[j] * x[j];
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/* L170: */
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}
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dxnorm = wa2.blueNorm();
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temp = fp;
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fp = dxnorm - delta;
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/* if the function is small enough, accept the current value */
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/* of par. also test for the exceptional cases where parl */
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/* is zero or the number of iterations has reached 10. */
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/* if the function is small enough, accept the current value */
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/* of par. also test for the exceptional cases where parl */
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/* is zero or the number of iterations has reached 10. */
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if (ei_abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) ||
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iter == 10) {
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goto L220;
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iter == 10) {
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goto L220;
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}
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/* compute the newton correction. */
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/* compute the newton correction. */
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for (j = 0; j < n; ++j) {
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l = ipvt[j]-1;
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wa1[j] = diag[l] * (wa2[l] / dxnorm);
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/* L180: */
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l = ipvt[j]-1;
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wa1[j] = diag[l] * (wa2[l] / dxnorm);
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/* L180: */
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}
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for (j = 0; j < n; ++j) {
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wa1[j] /= sdiag[j];
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temp = wa1[j];
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jp1 = j + 1;
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for (i = jp1; i < n; ++i)
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wa1[i] -= r__(i,j) * temp;
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wa1[j] /= sdiag[j];
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temp = wa1[j];
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jp1 = j + 1;
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for (i = jp1; i < n; ++i)
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wa1[i] -= r__(i,j) * temp;
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}
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temp = wa1.blueNorm();
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parc = fp / delta / temp / temp;
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/* depending on the sign of the function, update parl or paru. */
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/* depending on the sign of the function, update parl or paru. */
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if (fp > 0.) {
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parl = std::max(parl,par);
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parl = std::max(parl,par);
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}
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if (fp < 0.) {
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paru = std::min(paru,par);
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paru = std::min(paru,par);
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}
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/* compute an improved estimate for par. */
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/* compute an improved estimate for par. */
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/* Computing MAX */
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/* Computing MAX */
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par = std::max(parl,par+parc);
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/* end of an iteration. */
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/* end of an iteration. */
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goto L150;
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L220:
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/* termination. */
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/* termination. */
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if (iter == 0) {
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par = 0.;
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par = 0.;
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}
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return;
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/* last card of subroutine lmpar. */
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/* last card of subroutine lmpar. */
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} /* lmpar_ */
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