The optimum. We search for it.  
is the gradient of .  
is the Hessian matrix of .  
The Hessian Matrix of F at point  
The current approximation of the Hessian Matrix of F at point
. If not stated explicitly, we will always assume . 

The Hessian Matrix at the optimum point.  
is the quadratical approximation of around x. 
is the iteration index of the algorithm.  
is the direction of research. Conceptually, it's only a direction not a length.  
is the step performed at iteration .  
is the length of the step preformed at iteration k.  
the distance from the current point to the optimum. 
(2.1) 
(2.2) 
linear convergence  
superlinear convergence  with 
quadratic convergence 
(2.3) 
subject to 
0  
0  
0  
(2.12) 
(2.13) 
(2.14) 