202 research outputs found
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
(1230) and (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
Analysis and Application of Potential Energy Smoothing and Search Methods for Global Optimization
HLA Genes, Islet Autoantibodies and Residual C-Peptide at the Clinical Onset of Type 1 Diabetes Mellitus and the Risk of Retinopathy 15 Years Later
HLA genes, islet autoantibodies and residual C-peptide were studied to determine the independent association of each exposure with diabetic retinopathy (DR), 15 years after the clinical onset of type 1 diabetes in 15-34 year old individuals.The cohort was identified in 1992 and 1993 by the Diabetes Incidence Study in Sweden (DISS), which investigates incident cases of diabetes for patients between 15 and 34 years of age. Blood samples at diagnosis were analyzed to determine HLA genotype, islet autoantibodies and serum C-peptide. In 2009, fundus photographs were obtained from patient records. Study measures were supplemented with data from the Swedish National Diabetes Registry.The prevalence of DR was 60.2% (148/246). Autoantibodies against the 65 kD isoform of glutamate decarboxylase (GADA) at the onset of clinical diabetes increased the risk of DR 15 years later, relative risk 1.12 for each 100 WHO units/ml, [95% CI 1.02 to 1.23]. This equates to risk estimates of 1.27, [95% CI 1.04 to 1.62] and 1.43, [95% CI 1.06 to 1.94] for participants in the highest 25(th) (GADA>233 WHO units/ml) and 5(th) percentile (GADA>319 WHO units/ml) of GADA, respectively. These were adjusted for duration of diabetes, HbA(1c), treated hypertension, sex, age at diagnosis, HLA and C-peptide. Islet cell autoantibodies, insulinoma-antigen 2 autoantibodies, residual C-peptide and the type 1 diabetes associated haplotypes DQ2, DQ8 and DQ6 were not associated with DR.Increased levels of GADA at the onset of type 1 diabetes were associated with DR 15 years later. These results, if confirmed, could provide additional insights into the pathogenesis of the most common microvascular complication of diabetes and lead to better risk stratification for both patient screenings and DR treatment trials
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