Conjugate gradient method (Conjugate Gradient) between the steepest descent between law and Newton' s method is a method, it is only the first derivative information, but to overcome the steepest descent method of slow convergence shortcomings, but also avoid the Newton method needs to be stored and calculate the Hesse matrix and the inverse of the shortcomings of the conjugate gradient method is not only the most useful way to solve the large linear equations, one is also the solution of large-scale nonlinear optimization one of the most effective algorithm.

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