--- a/scripts/optimization/fsolve.m	Mon Jun 25 15:28:25 2018 -0400
+++ b/scripts/optimization/fsolve.m	Mon Jun 25 13:50:04 2018 -0700
@@ -352,7 +352,9 @@
         nfail += 1;
         delta *= decfac;
         decfac ^= 1.4142;
-        if (delta <= 1e1*macheps*xn)
+        if (fn <= tolf*n*xn)
+          info = 1;
+        elseif (delta <= 1e1*macheps*xn)
           ## Trust region became uselessly small.
           info = -3;
           break;
@@ -594,6 +596,23 @@
 %! assert (norm (f) < tol);
 %! assert (norm (x - x_opt, Inf) < tol);
 
+%!test <*53991>
+%! A = @(lam) [0 1 0 0; 0 0 1 0; 0 0 0 1; 0 0 -lam^2 0];
+%! C = [1 0 0 0; 0 0 1 0];
+%! B = @(lam) [C*expm(A(lam)*0); C*expm(A(lam)*1)];
+%! detB = @(lam) det (B(lam));
+%! 
+%! [x, fvec, info] = fsolve (detB, 0);
+%! assert (x == 0);
+%! assert (fvec == -1);
+%! assert (info == -2);
+
+%!test <*53991>
+%! [x, fvec, info] = fsolve (@(x) 5*x, 0);
+%! assert (x == 0);
+%! assert (fvec == 0);
+%! assert (info == 1);
+
 ## Solve the double dogleg trust-region least-squares problem:
 ## Minimize norm(r*x-b) subject to the constraint norm(d.*x) <= delta,
 ## x being a convex combination of the gauss-newton and scaled gradient.