Decomposing Isotonic Regression for Efficiently Solving Large Problems

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

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Ronny Luss, Saharon Rosset, Moni Shahar


A new algorithm for isotonic regression is presented based on recursively partitioning the solution space. We develop efficient methods for each partitioning subproblem through an equivalent representation as a network flow problem, and prove that this sequence of partitions converges to the global solution. These network flow problems can further be decomposed in order to solve very large problems. Success of isotonic regression in prediction and our algorithm's favorable computational properties are demonstrated through simulated examples as large as 2x10^5 variables and 10^7 constraints.