An Active-Set-Based Recursive Approach for Solving Convex Isotonic Regression with Generalized Order Restrictions

Author:

Chen Xuyu1,Li Xudong2,Su Yangfeng1

Affiliation:

1. School of Mathematical Sciences, Fudan University, Shanghai 200433, P. R. China

2. School of Data Science, Fudan University, Shanghai 200433, P. R. China

Abstract

This paper studies the convex isotonic regression with generalized order restrictions induced by a directed tree. The proposed model covers various intriguing optimization problems with shape or order restrictions, including the generalized nearly isotonic optimization and the total variation on a tree. Inspired by the success of the pool-adjacent-violator algorithm and its active-set interpretation, we propose an active-set-based recursive approach for solving the underlying model. Unlike the brute-force approach that traverses an exponential number of possible active-set combinations, our algorithm has a polynomial time computational complexity under mild assumptions.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Young Elite Scientists Sponsorship Program by CAST

Shanghai Education Development Foundation and Shanghai Municipal Education Commission

Shanghai Science and Technology Program

Publisher

World Scientific Pub Co Pte Ltd

Subject

Management Science and Operations Research,Management Science and Operations Research

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