Quasi-Newton methods are a class of optimization algorithms used to find the minimum or maximum of functions, especially useful in machine learning and neural networks. They are designed to approximate the Newton's method, which requires computing the Hessian matrix, but instead uses updates based on gradient information to achieve faster convergence without the high computational cost associated with directly calculating second-order derivatives.
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