Quasi-Newton methods are optimization techniques used to find local maxima or minima of functions without the need for calculating second derivatives. These methods are particularly useful in nonlinear programming as they build up an approximation of the Hessian matrix, which represents second-order partial derivatives, based on gradient information obtained from the function. By updating this approximation iteratively, quasi-Newton methods strike a balance between efficiency and accuracy in optimization problems.
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