Wolfe conditions are criteria used to ensure that a search direction in optimization methods leads to sufficient decrease in the objective function and that the step size is appropriate for maintaining convergence. These conditions help establish a framework for line search methods and are particularly important in algorithms like steepest descent and Newton's method, where finding an optimal step size significantly impacts efficiency and convergence properties.
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