A New Algorithm for the K\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^K$$\end{document}DMDGP Subclass of Distance Geometry Problems with Exact Distances

被引:0
作者
Douglas S. Gonçalves
Carlile Lavor
Leo Liberti
Michael Souza
机构
[1] CFM,
[2] Federal University of Santa Catarina,undefined
[3] IMECC,undefined
[4] University of Campinas,undefined
[5] LIX CNRS École Polytechnique,undefined
[6] Institut Polytechnique de Paris,undefined
[7] Federal University of Ceará,undefined
关键词
Distance Geometry; Discretization; Symmetries; DMDGP;
D O I
10.1007/s00453-021-00835-6
中图分类号
学科分类号
摘要
The fundamental inverse problem in distance geometry is the one of finding positions from inter-point distances. The Discretizable Molecular Distance Geometry Problem (DMDGP) is a subclass of the Distance Geometry Problem (DGP) whose search space can be discretized and represented by a binary tree, which can be explored by a Branch-and-Prune (BP) algorithm. It turns out that this combinatorial search space possesses many interesting symmetry properties that were studied in the last decade. In this paper, we present a new algorithm for this subclass of the DGP, which exploits DMDGP symmetries more effectively than its predecessors. Computational results show that the speedup, with respect to the classic BP algorithm, is considerable for sparse DMDGP instances related to protein conformation.
引用
收藏
页码:2400 / 2426
页数:26
相关论文
共 97 条
  • [1] Alencar J(2019)Realizing Euclidean distance matrices by sphere intersection Discret. Appl. Math. 256 5-10
  • [2] Lavor C(2020)On the estimation of unknown distances for a class of Euclidean distance matrix completion problems with interval data Linear Algebra Appl. 592 287-305
  • [3] Liberti L(2000)The protein data bank Nucl. Acids Res. 28 235-242
  • [4] Baez-Sanchez A(2018)Recent results on assigned and unassigned distance geometry with applications to protein molecules and nanostructures Ann. Oper. Res. 271 161-203
  • [5] Lavor C(2017)Calculating the possible conformations arising from uncertainty in the molecular distance geometry problem using constraint interval analysis Inf. Sci. 415 41-52
  • [6] Berman HM(2015)Euclidean distance matrices: essential theory, algorithms, and applications IEEE Signal Proc. Magaz. 32 12-30
  • [7] Westbrook J(2003)A geometric build-up algorithm for solving the molecular distance geometry problem with sparse distance data J. Global Optim. 26 321-333
  • [8] Feng Z(2018)A symmetry-based splitting strategy for discretizable distance geometry problems J. Global Optim. 71 717-733
  • [9] Gilliland G(2010)Explicit sensor network localization using semidefinite representations and facial reductions SIAM J. Optim. 20 2679-2708
  • [10] Bhat TN(2019)Minimal NMR distance information for rigidity of protein graphs Discret. Appl. Math. 256 91-104