Semi‐labeled unrooted binary tree optimization subject to nonnegativity
Let Dn×n denote the distance matrix of n objects, and let T be an unrooted binary tree in which the leaves denote those n objects. We want to find such a tree with the constraint that the edge weights are nonnegative where the distances between the leaves best estimate their corresponding values in...
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| Published in | Networks Vol. 80; no. 2; pp. 249 - 263 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.09.2022
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| ISSN | 0028-3045 1097-0037 1097-0037 |
| DOI | 10.1002/net.22090 |
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| Abstract | Let Dn×n denote the distance matrix of n objects, and let T be an unrooted binary tree in which the leaves denote those n objects. We want to find such a tree with the constraint that the edge weights are nonnegative where the distances between the leaves best estimate their corresponding values in D. Accordingly, we have adopted the residual sum of squares (RSS) criterion to minimize the discrepancy between the distance between leaves in the tree and their corresponding distance in D. For this optimization problem, we have designed an iterated local search (ILS) scheme based on the nearest neighbor interchange (NNI) operation to search the neighborhood. |
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| AbstractList | Let denote the distance matrix of
n
objects, and let
T
be an unrooted binary tree in which the leaves denote those
n
objects. We want to find such a tree with the constraint that the edge weights are nonnegative where the distances between the leaves best estimate their corresponding values in
D
. Accordingly, we have adopted the residual sum of squares (RSS) criterion to minimize the discrepancy between the distance between leaves in the tree and their corresponding distance in
D
. For this optimization problem, we have designed an iterated local search (ILS) scheme based on the nearest neighbor interchange (NNI) operation to search the neighborhood. Let Dn×n denote the distance matrix of n objects, and let T be an unrooted binary tree in which the leaves denote those n objects. We want to find such a tree with the constraint that the edge weights are nonnegative where the distances between the leaves best estimate their corresponding values in D. Accordingly, we have adopted the residual sum of squares (RSS) criterion to minimize the discrepancy between the distance between leaves in the tree and their corresponding distance in D. For this optimization problem, we have designed an iterated local search (ILS) scheme based on the nearest neighbor interchange (NNI) operation to search the neighborhood. |
| Author | Wormald, Nick Hosseini, Seyed Soheil |
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| Cites_doi | 10.1088/1742-5468/2015/12/P12005 10.1016/j.physa.2015.10.108 10.1038/srep01665 10.1016/j.physa.2011.01.020 10.1073/pnas.0802089105 10.1103/PhysRevE.68.056110 10.1016/j.physrep.2009.11.002 10.1016/j.physa.2010.04.002 10.1093/sysbio/46.1.101 10.1140/epjb/e2002-00380-9 10.1016/j.physa.2014.03.083 10.1007/s10614-017-9672-x 10.1016/j.physa.2011.07.023 10.1007/978-88-470-2553-0_16 10.1016/j.physa.2016.07.029 10.1016/j.jedc.2007.01.034 10.1007/s10614-015-9481-z 10.1016/j.jempfin.2016.06.003 10.1093/oxfordjournals.molbev.a025863 10.1016/j.physa.2018.05.039 10.1137/S0895480192237403 10.1016/j.physa.2019.04.128 10.1007/s100510050929 |
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| Snippet | Let Dn×n denote the distance matrix of n objects, and let T be an unrooted binary tree in which the leaves denote those n objects. We want to find such a tree... Let denote the distance matrix of n objects, and let T be an unrooted binary tree in which the leaves denote those n objects. We want to find such a tree with... |
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| SubjectTerms | Iterated local search neighborhood search nonnegativity constraint optimal weighted tree Optimization residual sum of squares unrooted binary tree optimization |
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| Title | Semi‐labeled unrooted binary tree optimization subject to nonnegativity |
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