--- a
+++ b/combinedDeepLearningActiveContour/minFunc/rosenbrock.m
@@ -0,0 +1,40 @@
+function [f, df, ddf, dddf] = rosenbrock(x);
+
+% rosenbrock.m This function returns the function value, partial derivatives
+% and Hessian of the (general dimension) rosenbrock function, given by:
+%
+%       f(x) = sum_{i=1:D-1} 100*(x(i+1) - x(i)^2)^2 + (1-x(i))^2 
+%
+% where D is the dimension of x. The true minimum is 0 at x = (1 1 ... 1).
+%
+% Carl Edward Rasmussen, 2001-07-21.
+
+D = length(x);
+f = sum(100*(x(2:D)-x(1:D-1).^2).^2 + (1-x(1:D-1)).^2);
+
+if nargout > 1
+  df = zeros(D, 1);
+  df(1:D-1) = - 400*x(1:D-1).*(x(2:D)-x(1:D-1).^2) - 2*(1-x(1:D-1));
+  df(2:D) = df(2:D) + 200*(x(2:D)-x(1:D-1).^2);
+end
+
+if nargout > 2
+  ddf = zeros(D,D);
+  ddf(1:D-1,1:D-1) = diag(-400*x(2:D) + 1200*x(1:D-1).^2 + 2);
+  ddf(2:D,2:D) = ddf(2:D,2:D) + 200*eye(D-1);
+  ddf = ddf - diag(400*x(1:D-1),1) - diag(400*x(1:D-1),-1);
+end
+
+if nargout > 3
+    dddf = zeros(D,D,D);
+    for d = 1:D
+       if d > 1
+           dddf(d,d-1,d-1) = -400;
+       end
+       if d < D
+          dddf(d,d+1,d) = -400;
+          dddf(d,d,d+1) = -400;
+          dddf(d,d,d) = 2400*x(d);
+       end
+    end
+end
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