Introduction to Conjugate Gradient
Conjugate Gradient is a method between the steepest descent method and Newton's method. It only needs to use the first derivative information, but it overcomes the shortcomings of slow convergence of the steepest descent method and avoids the need for storage in Newton's method. In addition to the shortcomings of calculating the Hesse matrix and finding the inverse, the conjugate gradient method is not only one of the most useful methods for solving large linear equations, but also one of the most effective algorithms for solving large nonlinear optimization.