Gradient-based methods are optimization techniques that utilize the gradient (or derivative) of a function to find its local minima or maxima. These methods are widely used in various fields, including inverse problems, because they efficiently navigate the parameter space by following the steepest descent direction, which is determined by the gradient of the objective function.
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