Global Continuation for Distance Geometry Problems

Authors:
Jorge J. Moré;Zhijun Wu
Affiliations:
-;-
Venue:
SIAM Journal on Optimization
Year:
1997

Citing 0
Cited 25

A linear-time algorithm for solving the molecular distance geometry problem with exact inter-atomic distances

Journal of Global Optimization
Selective Search for Global Optimization of Zero or Small Residual Least-Squares Problems: A Numerical Study

Computational Optimization and Applications
Some Properties for the Euclidean Distance Matrix and Positive Semidefinite Matrix Completion Problems

Journal of Global Optimization
Solving Large Scale Molecular Distance Geometry Problems by a Smoothing Technique via the Gaussian Transform and D.C. Programming

Journal of Global Optimization
Semidefinite programming for ad hoc wireless sensor network localization

Proceedings of the 3rd international symposium on Information processing in sensor networks
Theory of semidefinite programming for sensor network localization

SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
A semidefinite programming approach to tensegrity theory and realizability of graphs

SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
A Smoothing Method of Global Optimization that Preserves Global Minima

Journal of Global Optimization
Semidefinite programming based algorithms for sensor network localization

ACM Transactions on Sensor Networks (TOSN)
A Trust-Region Algorithm for Global Optimization

Computational Optimization and Applications
Extending the geometric build-up algorithm for the molecular distance geometry problem

Information Processing Letters
Double variable neighbourhood search with smoothing for the molecular distance geometry problem

Journal of Global Optimization
Solving molecular distance geometry problems by global optimization algorithms

Computational Optimization and Applications
Sum of squares method for sensor network localization

Computational Optimization and Applications
Curriculum learning

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distributed algorithm for node localization in wireless ad-hoc networks

ACM Transactions on Sensor Networks (TOSN)
Learning Deep Architectures for AI

Foundations and Trends® in Machine Learning
Reformulating the least-square source localization problem with contracted distances

Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
An algorithm for nonlinear optimization problems with binary variables

Computational Optimization and Applications
An SDP-Based Divide-and-Conquer Algorithm for Large-Scale Noisy Anchor-Free Graph Realization

SIAM Journal on Scientific Computing
On the computation of protein backbones by using artificial backbones of hydrogens

Journal of Global Optimization
Protein structure by semidefinite facial reduction

RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
The discretizable molecular distance geometry problem

Computational Optimization and Applications
Edge-based semidefinite programming relaxation of sensor network localization with lower bound constraints

Computational Optimization and Applications
Canonical dual solutions to nonconvex radial basis neural network optimization problem

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Distance geometry problems arise in the determination of protein structure. We consider the case where only a subset of the distances between atoms is given and formulate this distance geometry problem as a global minimization problem with special structure. We show that global smoothing techniques and a continuation approach for global optimization can be used to determine global solutions of this problem reliably and efficiently. The global continuation approach determines a global solution with less computational effort than is required by a standard multistart algorithm. Moreover, the continuation approach usually finds the global solution from any given starting point, while the multistart algorithm tends to fail.