Second-order conditions refer to a set of criteria used to determine the nature of critical points in optimization problems, especially when assessing whether these points are minima, maxima, or saddle points. These conditions extend the first-order conditions, which focus on the gradients, by incorporating information about the curvature of the objective function through the Hessian matrix. In infinite-dimensional spaces, these conditions help in analyzing more complex variational problems by providing insights into stability and sensitivity near critical points.
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