Description: Conjugate Gradient Method (Conjugate Gradient) is between the steepest descent method between a law and Newton' s method, it is only the first order derivative information, but the steepest descent method overcomes the shortcomings of slow convergence and avoid the need to store the Newton law Hesse and cons of computing the inverse matrix and the conjugate gradient method is not only one of the most useful methods to solve large linear equations, but also large-scale nonlinear optimization solution of one of the most effective algorithm.
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