### Abstract

We consider the problem of computing the Euclidean projection of a vector of length p onto a non-negative max-heap-an ordered tree where the values of the nodes are all nonnegative and the value of any parent node is no less than the value(s) of its child node(s). This Euclidean projection plays a building block role in the optimization problem with a non-negative maxheap constraint. Such a constraint is desirable when the features follow an ordered tree structure, that is, a given feature is selected for the given regression/classification task only if its parent node is selected. In this paper, we show that such Euclidean projection problem admits an analytical solution and we develop a top-down algorithm where the key operation is to find the so-called maximal root-tree of the subtree rooted at each node. A naive approach for finding the maximal root-tree is to enumerate all the possible root-trees, which, however, does not scale well. We reveal several important properties of the maximal root-tree, based on which we design a bottom-up algorithm with merge for efficiently finding the maximal roottree. The proposed algorithm has a (worst-case) linear time complexity for a sequential list, and O(p ^{2}) for a general tree. We report simulation results showing the effectiveness of the max-heap for regression with an ordered tree structure. Empirical results show that the proposed algorithm has an expected linear time complexity for many special cases including a sequential list, a full binary tree, and a tree with depth 1.

Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 |

State | Published - 2011 |

Event | 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 - Granada, Spain Duration: Dec 12 2011 → Dec 14 2011 |

### Other

Other | 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 |
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Country | Spain |

City | Granada |

Period | 12/12/11 → 12/14/11 |

### Fingerprint

### ASJC Scopus subject areas

- Information Systems

### Cite this

*Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011*

**Projection onto A nonnegative max-heap.** / Liu, Jun; Sun, Liang; Ye, Jieping.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011.*25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011, Granada, Spain, 12/12/11.

}

TY - GEN

T1 - Projection onto A nonnegative max-heap

AU - Liu, Jun

AU - Sun, Liang

AU - Ye, Jieping

PY - 2011

Y1 - 2011

N2 - We consider the problem of computing the Euclidean projection of a vector of length p onto a non-negative max-heap-an ordered tree where the values of the nodes are all nonnegative and the value of any parent node is no less than the value(s) of its child node(s). This Euclidean projection plays a building block role in the optimization problem with a non-negative maxheap constraint. Such a constraint is desirable when the features follow an ordered tree structure, that is, a given feature is selected for the given regression/classification task only if its parent node is selected. In this paper, we show that such Euclidean projection problem admits an analytical solution and we develop a top-down algorithm where the key operation is to find the so-called maximal root-tree of the subtree rooted at each node. A naive approach for finding the maximal root-tree is to enumerate all the possible root-trees, which, however, does not scale well. We reveal several important properties of the maximal root-tree, based on which we design a bottom-up algorithm with merge for efficiently finding the maximal roottree. The proposed algorithm has a (worst-case) linear time complexity for a sequential list, and O(p 2) for a general tree. We report simulation results showing the effectiveness of the max-heap for regression with an ordered tree structure. Empirical results show that the proposed algorithm has an expected linear time complexity for many special cases including a sequential list, a full binary tree, and a tree with depth 1.

AB - We consider the problem of computing the Euclidean projection of a vector of length p onto a non-negative max-heap-an ordered tree where the values of the nodes are all nonnegative and the value of any parent node is no less than the value(s) of its child node(s). This Euclidean projection plays a building block role in the optimization problem with a non-negative maxheap constraint. Such a constraint is desirable when the features follow an ordered tree structure, that is, a given feature is selected for the given regression/classification task only if its parent node is selected. In this paper, we show that such Euclidean projection problem admits an analytical solution and we develop a top-down algorithm where the key operation is to find the so-called maximal root-tree of the subtree rooted at each node. A naive approach for finding the maximal root-tree is to enumerate all the possible root-trees, which, however, does not scale well. We reveal several important properties of the maximal root-tree, based on which we design a bottom-up algorithm with merge for efficiently finding the maximal roottree. The proposed algorithm has a (worst-case) linear time complexity for a sequential list, and O(p 2) for a general tree. We report simulation results showing the effectiveness of the max-heap for regression with an ordered tree structure. Empirical results show that the proposed algorithm has an expected linear time complexity for many special cases including a sequential list, a full binary tree, and a tree with depth 1.

UR - http://www.scopus.com/inward/record.url?scp=84860625196&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860625196&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84860625196

SN - 9781618395993

BT - Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

ER -