The Pool Adjacent Violators Algorithm (PAVA) is a method used to solve isotonic regression problems, where the goal is to fit a non-decreasing function to a set of data points. It efficiently adjusts adjacent violators in the data to maintain order constraints, making it widely applicable in statistical and machine learning tasks involving monotonicity constraints.
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