56,106 research outputs found
On the Thermodynamics of Global Optimization
Theoretical design of global optimization algorithms can profitably utilize
recent statistical mechanical treatments of potential energy surfaces (PES's).
Here we analyze a particular method to explain its success in locating global
minima on surfaces with a multiple-funnel structure, where trapping in local
minima with different morphologies is expected. We find that a key factor in
overcoming trapping is the transformation applied to the PES which broadens the
thermodynamic transitions. The global minimum then has a significant
probability of occupation at temperatures where the free energy barriers
between funnels are surmountable.Comment: 4 pages, 3 figures, revte
Evolution of the Potential Energy Surface with Size for Lennard-Jones Clusters
Disconnectivity graphs are used to characterize the potential energy surfaces
of Lennard-Jones clusters containing 13, 19, 31, 38, 55 and 75 atoms. This set
includes members which exhibit either one or two `funnels' whose low-energy
regions may be dominated by a single deep minimum or contain a number of
competing structures. The graphs evolve in size due to these specific size
effects and an exponential increase in the number of local minima with the
number of atoms. To combat the vast number of minima we investigate the use of
monotonic sequence basins as the fundamental topographical unit. Finally, we
examine disconnectivity graphs for a transformed energy landscape to explain
why the transformation provides a useful approach to the global optimization
problem.Comment: 13 pages, 8 figures, revte
Subspace-Based Holistic Registration for Low-Resolution Facial Images
Subspace-based holistic registration is introduced as an alternative to landmark-based face registration, which has a poor performance on low-resolution images, as obtained in camera surveillance applications. The proposed registration method finds the alignment by maximizing the similarity score between a probe and a gallery image. We use a novel probabilistic framework for both user-independent as well as user-specific face registration. The similarity is calculated using the probability that the face image is correctly aligned in a face subspace, but additionally we take the probability into account that the face is misaligned based on the residual error in the dimensions perpendicular to the face subspace. We perform extensive experiments on the FRGCv2 database to evaluate the impact that the face registration methods have on face recognition. Subspace-based holistic registration on low-resolution images can improve face recognition in comparison with landmark-based registration on high-resolution images. The performance of the tested face recognition methods after subspace-based holistic registration on a low-resolution version of the FRGC database is similar to that after manual registration
Thermodynamics and the Global Optimization of Lennard-Jones clusters
Theoretical design of global optimization algorithms can profitably utilize
recent statistical mechanical treatments of potential energy surfaces (PES's).
Here we analyze the basin-hopping algorithm to explain its success in locating
the global minima of Lennard-Jones (LJ) clusters, even those such as \LJ{38}
for which the PES has a multiple-funnel topography, where trapping in local
minima with different morphologies is expected. We find that a key factor in
overcoming trapping is the transformation applied to the PES which broadens the
thermodynamic transitions. The global minimum then has a significant
probability of occupation at temperatures where the free energy barriers
between funnels are surmountable.Comment: 13 pages, 13 figures, revte
Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms
We describe a global optimization technique using `basin-hopping' in which
the potential energy surface is transformed into a collection of
interpenetrating staircases. This method has been designed to exploit the
features which recent work suggests must be present in an energy landscape for
efficient relaxation to the global minimum. The transformation associates any
point in configuration space with the local minimum obtained by a geometry
optimization started from that point, effectively removing transition state
regions from the problem. However, unlike other methods based upon hypersurface
deformation, this transformation does not change the global minimum. The lowest
known structures are located for all Lennard-Jones clusters up to 110 atoms,
including a number that have never been found before in unbiased searches.Comment: 8 pages, 3 figures, revte
A path following algorithm for the graph matching problem
We propose a convex-concave programming approach for the labeled weighted
graph matching problem. The convex-concave programming formulation is obtained
by rewriting the weighted graph matching problem as a least-square problem on
the set of permutation matrices and relaxing it to two different optimization
problems: a quadratic convex and a quadratic concave optimization problem on
the set of doubly stochastic matrices. The concave relaxation has the same
global minimum as the initial graph matching problem, but the search for its
global minimum is also a hard combinatorial problem. We therefore construct an
approximation of the concave problem solution by following a solution path of a
convex-concave problem obtained by linear interpolation of the convex and
concave formulations, starting from the convex relaxation. This method allows
to easily integrate the information on graph label similarities into the
optimization problem, and therefore to perform labeled weighted graph matching.
The algorithm is compared with some of the best performing graph matching
methods on four datasets: simulated graphs, QAPLib, retina vessel images and
handwritten chinese characters. In all cases, the results are competitive with
the state-of-the-art.Comment: 23 pages, 13 figures,typo correction, new results in sections 4,5,
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