--- a
+++ b/combinedDeepLearningActiveContour/minFunc/lbfgs.m
@@ -0,0 +1,40 @@
+function [d] = lbfgs(g,s,y,Hdiag)
+% BFGS Search Direction
+%
+% This function returns the (L-BFGS) approximate inverse Hessian,
+% multiplied by the gradient
+%
+% If you pass in all previous directions/sizes, it will be the same as full BFGS
+% If you truncate to the k most recent directions/sizes, it will be L-BFGS
+%
+% s - previous search directions (p by k)
+% y - previous step sizes (p by k)
+% g - gradient (p by 1)
+% Hdiag - value of initial Hessian diagonal elements (scalar)
+
+[p,k] = size(s);
+
+for i = 1:k
+    ro(i,1) = 1/(y(:,i)'*s(:,i));
+end
+
+q = zeros(p,k+1);
+r = zeros(p,k+1);
+al =zeros(k,1);
+be =zeros(k,1);
+
+q(:,k+1) = g;
+
+for i = k:-1:1
+    al(i) = ro(i)*s(:,i)'*q(:,i+1);
+    q(:,i) = q(:,i+1)-al(i)*y(:,i);
+end
+
+% Multiply by Initial Hessian
+r(:,1) = Hdiag*q(:,1);
+
+for i = 1:k
+    be(i) = ro(i)*y(:,i)'*r(:,i);
+    r(:,i+1) = r(:,i) + s(:,i)*(al(i)-be(i));
+end
+d=r(:,k+1);
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