further cleaning/ goto removing

unsupported/Eigen/src/NonLinear/dogleg.h

@@ -39,13 +39,11 @@ void ei_dogleg(
                 /* Computing MAX */
                 temp = std::max(temp,ei_abs(r[l]));
                 l = l + n - i;
-                /* L30: */
             }
             temp = epsmch * temp;
-            if (temp == 0.) {
+            if (temp == 0.)
                 temp = epsmch;
         }
-        }
         x[j] = (qtb[j] - sum) / temp;
     }
 
@@ -77,7 +75,7 @@ void ei_dogleg(
     sgnorm = 0.;
     alpha = delta / qnorm;
     if (gnorm == 0.)
-        goto L120;
+        goto algo_end;
 
     /* calculate the point along the scaled gradient */
     /* at which the quadratic is minimized. */
@@ -100,9 +98,8 @@ void ei_dogleg(
     /* test whether the scaled gradient direction is acceptable. */
 
     alpha = 0.;
-    if (sgnorm >= delta) {
-        goto L120;
-    }
+    if (sgnorm >= delta)
+        goto algo_end;
 
     /* the scaled gradient direction is not acceptable. */
     /* finally, calculate the point along the dogleg */
@@ -114,7 +111,7 @@ void ei_dogleg(
     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)));
     /* Computing 2nd power */
     alpha = delta / qnorm * (1. - ei_abs2(sgnorm / delta)) / temp;
-L120:
+algo_end:
 
     /* form appropriate convex combination of the gauss-newton */
     /* direction and the scaled gradient direction. */

unsupported/Eigen/src/NonLinear/lmpar.h

@@ -62,16 +62,17 @@ void ei_lmpar(
     wa2 = diag.cwise() * x;
     dxnorm = wa2.blueNorm();
     fp = dxnorm - delta;
-    if (fp <= Scalar(0.1) * delta)
-        goto L220;
+    if (fp <= Scalar(0.1) * delta) {
+        par = 0;
+        return;
+    }
 
     /* if the jacobian is not rank deficient, the newton */
     /* step provides a lower bound, parl, for the zero of */
     /* the function. otherwise set this bound to zero. */
 
     parl = 0.;
-    if (nsing < n-1)
-        goto L120;
+    if (nsing >= n-1) {
         for (j = 0; j < n; ++j) {
             l = ipvt[j];
             wa1[j] = diag[l] * (wa2[l] / dxnorm);
@@ -84,7 +85,7 @@ void ei_lmpar(
         }
         temp = wa1.blueNorm();
         parl = fp / delta / temp / temp;
-L120:
+    }
 
     /* calculate an upper bound, paru, for the zero of the function. */
 
@@ -97,9 +98,8 @@ L120:
     }
     gnorm = wa1.stableNorm();
     paru = gnorm / delta;
-    if (paru == 0.) {
+    if (paru == 0.)
         paru = dwarf / std::min(delta,Scalar(0.1));
-    }
 
     /* if the input par lies outside of the interval (parl,paru), */
     /* set par to the closer endpoint. */
@@ -111,7 +111,7 @@ L120:
 
     /* beginning of an iteration. */
 
-L150:
+    while (true) {
         ++iter;
 
         /* evaluate the function at the current value of par. */
@@ -135,10 +135,8 @@ L150:
         /* of par. also test for the exceptional cases where parl */
         /* is zero or the number of iterations has reached 10. */
 
-    if (ei_abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) ||
-            iter == 10) {
-        goto L220;
-    }
+        if (ei_abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
+            break;
 
         /* compute the newton correction. */
 
@@ -158,12 +156,10 @@ L150:
 
         /* depending on the sign of the function, update parl or paru. */
 
-    if (fp > 0.) {
+        if (fp > 0.)
             parl = std::max(parl,par);
-    }
-    if (fp < 0.) {
+        if (fp < 0.)
             paru = std::min(paru,par);
-    }
 
         /* compute an improved estimate for par. */
 
@@ -172,14 +168,12 @@ L150:
 
         /* end of an iteration. */
 
-    goto L150;
-L220:
+    }
 
     /* termination. */
 
-    if (iter == 0) {
+    if (iter == 0)
         par = 0.;
-    }
     return;
 }