A gradient-like method is an optimization technique that mimics the behavior of traditional gradient methods but is specifically designed to handle nonsmooth functions. These methods utilize generalized derivatives, such as subgradients or Clarke's generalized gradients, to navigate through non-differentiable points while maintaining a form of directional guidance similar to that of gradient descent. This makes them particularly useful for problems where standard gradients may not be applicable due to the presence of nonsmooth characteristics.
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