The proximal point algorithm is an iterative optimization method used to find minima of convex functions by leveraging the concept of proximity. It works by introducing a 'proximal' term that helps to regularize the optimization problem, allowing for more stable convergence properties. This method is particularly useful in scenarios where the objective function is non-differentiable, connecting closely to subgradients and subdifferentials as it relies on these concepts to navigate the solution space.
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