Proximal gradient methods are numerical optimization techniques that combine the principles of gradient descent with proximal operators to solve optimization problems that involve non-smooth functions. These methods are particularly useful in scenarios where the objective function is a sum of a smooth term and a non-smooth regularization term, allowing for effective handling of constraints or promoting sparsity in solutions.
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