Quasi-Newton methods are a class of iterative optimization algorithms that are used to find local minima or maxima of functions. They approximate the Hessian matrix, which represents second-order derivatives, to create a search direction that is more efficient than first-order methods. By updating an estimate of the inverse Hessian matrix at each iteration, these methods combine the advantages of Newton's method with reduced computational costs, making them suitable for large-scale optimization problems and variational inequalities.
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