Single temperature for Monte Carlo optimization on complex landscapes

We propose a new strategy for Monte Carlo (MC) optimization on rugged multidimensional landscapes. The strategy is based on querying the statistical properties of the landscape in order to find the temperature at which the mean first passage time across the current region of the landscape is minimized. Thus, in contrast to other algorithms such as simulated annealing (SA), we explicitly match the temperature schedule to the statistics of landscape irregularities. In cases where this statistics is approximately the same over the entire landscape, or where non-local moves couple distant parts of the landscape, single-temperature MC will outperform any other MC algorithm with the same move set. We also find that in strongly anisotropic Coulomb spin glass and traveling salesman problems, the only relevant statistics (which we use to assign a single MC temperature) is that of irregularities in low-energy funnels. Our results may explain why protein folding in nature is efficient at room temperatures.Comment: 5 pages, 3 figure

Understanding the impact of constraints: A rank based fitness function for evolutionary methods

There are design problems where some constraints may be considered objectives as in “It would be great if the solution we obtained had this characteristic.” In such problems, solutions obtained using multi-objective optimisation may help the decision maker gain insight into what is achievable without fully satisfying one of these constraints. A novel fitness function is introduced into a multi-objective population based evolutionary optimisation method, based on a plant propagation algorithm extended to multi-objective optimisation. The optimisation method is implemented and applied to the design of off-grid integrated energy systems for large scale mining operations where the aim is to use local renewable energy generation, coupled with energy storage, to eliminate the need for transporting fuel over large distances. The latter is a desired property and in this chapter is treated as a separate objective. The results presented show that the fitness function provides the desired selection pressure and, when combined with the multi-objective plant propagation algorithm, is able to find good designs that achieve the desired constraint simultaneously

Properties of Nucleon Resonances by means of a Genetic Algorithm

We present an optimization scheme that employs a Genetic Algorithm (GA) to determine the properties of low-lying nucleon excitations within a realistic photo-pion production model based upon an effective Lagrangian. We show that with this modern optimization technique it is possible to reliably assess the parameters of the resonances and the associated error bars as well as to identify weaknesses in the models. To illustrate the problems the optimization process may encounter, we provide results obtained for the nucleon resonances $\Delta$(1230) and $\Delta$(1700). The former can be easily isolated and thus has been studied in depth, while the latter is not as well known experimentally.Comment: 12 pages, 10 figures, 3 tables. Minor correction

Constant-time solution to the Global Optimization Problem using Bruschweiler's ensemble search algorithm

A constant-time solution of the continuous Global Optimization Problem (GOP) is obtained by using an ensemble algorithm. We show that under certain assumptions, the solution can be guaranteed by mapping the GOP onto a discrete unsorted search problem, whereupon Bruschweiler's ensemble search algorithm is applied. For adequate sensitivities of the measurement technique, the query complexity of the ensemble search algorithm depends linearly on the size of the function's domain. Advantages and limitations of an eventual NMR implementation are discussed.Comment: 14 pages, 0 figure

Multi-step Multi-camera View Planning for Real-Time Visual Object Tracking

Abstract. We present a new method for planning the optimal next view for a probabilistic visual object tracking task. Our method uses a variable number of cameras, can plan an action sequence several time steps into the future, and allows for real-time usage due to a computation time which is linear both in the number of cameras and the number of time steps. The algorithm can also handle object loss in one, more or all cameras, interdependencies in the camera’s information contribution, and variable action costs. We evaluate our method by comparing it to previous approaches with a prere-corded sequence of real world images. From K. Franke et al., Pattern Recognition, 28th DAGM Symposium, Springer, 2006, (pp. 536–545).

Quantum-entanglement aspects of polaron systems

We describe quantum entanglement inherent to the polaron ground states of coupled electron-phonon (or, more generally, particle-phonon) systems based on a model comprising both local (Holstein-type) and nonlocal (Peierls-type) coupling. We study this model using a variational method supplemented by the exact numerical diagonalization on a system of finite size. By way of subsequent numerical diagonalization of the reduced density matrix, we determine the particle-phonon entanglement as given by the von Neumann and linear entropies. Our results are strongly indicative of the intimate relationship between the particle localization/delocalization and the particle-phonon entanglement. In particular, we find a compelling evidence for the existence of a nonanalyticity in the entanglement entropies with respect to the Peierls-coupling strength. The occurrence of such nonanalyticity -- not accompanied by an actual quantum phase transition -- reinforces analogous conclusion drawn in several recent studies of entanglement in the realm of quantum-dissipative systems. In addition, we demonstrate that the entanglement entropies saturate inside the self-trapped region where the small-polaron states are nearly maximally mixed.Comment: Selected PRB Editors' Suggestion in 1 Dec Issu

Country-specific birth weight and length in type 1 diabetes high-risk HLA genotypes in combination with prenatal characteristics

Objective:To examine the relationship between high-risk human leukocyte antigen (HLA) genotypes for type 1 diabetes and birth size in combination with prenatal characteristics in different countries.Study Design:Four high-risk HLA genotypes were enrolled in the Environmental determinants of Diabetes in the Young study newborn babies from the general population in Finland, Germany, Sweden and the United States. Stepwise regression analyses were used to adjust for country, parental physical characteristics and environmental factors during pregnancy.Result:Regression analyses did not reveal differences in birth size between the four type 1 diabetes high-risk HLA genotypes. Compared with DQ 4/8 in each country, (1) DQ 2/2 children were heavier in the United States (P=0.028) mostly explained however, by parental weight; (2) DQ 2/8 (P=0.023) and DQ 8/8 (P=0.046) children were longer in Sweden independent of parents height and as well as (3) in the United States for DQ 2/8 (P=0.023), but again dependent on parental height.Conclusion:Children born with type 1 diabetes high-risk HLA genotypes have comparable birth size. Longitudinal follow-up of these children should reveal whether birth size differences between countries contribute to the risk for islet autoimmunity and type 1 diabetes.Journal of Perinatology advance online publication, 28 April 2011; doi:10.1038/jp.2011.26