The quasi-newton method is an optimization technique used to find the local minima of a function without requiring the computation of the Hessian matrix, instead approximating it using information from previous iterations. This method balances efficiency and accuracy by updating an estimate of the inverse Hessian matrix at each step, making it particularly effective for large-scale problems where traditional Newton's method would be too costly. It is closely related to limited-memory methods, such as L-BFGS, which store only a few vectors that represent the approximate curvature of the objective function.
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