arXiv subjects

Alexander K. Hartmann

Publications and source records attributed to Alexander K. Hartmann.

At least 91 records · Page 5Linked to original sources

arXiv2012

Excitations in high-dimensional random-field Ising magnets

Domain walls and droplet-like excitation of the random-field Ising magnet are studied in d={3,4,5,6,7} dimensions by means of exact numerical ground-state calculations. They are obtained using the established mapping to the graph-theoretical maximum-flow problem. This allows to study large system sizes of more than five million spins in exact thermal equilibrium. All simulations are carried out at the critical point for the strength h of the random fields, h=h_c(d), respectively. Using finite-size scaling, energetic and geometric properties like stiffness exponents and fractal dimensions are calculated. Using these results, we test (hyper) scaling relations, which seem to be fulfilled below the upper critical dimension d_u=6. Also, for d<d_u, the stiffness exponent can be obtained from the scaling of the ground-state energy.

cond-mat.dis-nn↗

arXiv2011

Bias in generation of random graphs

We study the statistical properties of the generation of random graphs according the configuration model, where one assigns randomly degrees to nodes. This model is often used, e.g., for the scale-free degree distribution ~d^gamma. For the efficient variant, where non-feasible edges are rejected and the construction of a graph continues, there exists a bias, which we calculate explicitly for a small sample ensemble. We find that this bias does not disappear with growing system size. This becomes also visible, e.g., for scale-free graphs when measuring quantities like the graph diameter. Hence, the efficient generation of general scale-free graphs with a very broad distribution (gamma <2) remains an open problem.

cond-mat.dis-nn↗

arXiv2011

Critical behavior of the Random-Field Ising Magnet with long range correlated disorder

We study the correlated-disorder driven zero-temperature phase transition of the Random-Field Ising Magnet using exact numerical ground-state calculations for cubic lattices. We consider correlations of the quenched disorder decaying proportional to r^a, where r is the distance between two lattice sites and a<0. To obtain exact ground states, we use a well established mapping to the graph-theoretical maximum-flow problem, which allows us to study large system sizes of more than two million spins. We use finite-size scaling analyses for values a={-1,-2,-3,-7} to calculate the critical point and the critical exponents characterizing the behavior of the specific heat, magnetization, susceptibility and of the correlation length close to the critical point. We find basically the same critical behavior as for the RFIM with delta-correlated disorder, except for the finite-size exponent of the susceptibility and for the case a=-1, where the results are also compatible with a phase transition at infinitesimal disorder strength. A summary of this work can be found at the papercore database at www.papercore.org.

cond-mat.dis-nn↗

arXiv2010

Critical behavior of the Random-Field Ising model at and beyond the Upper Critical Dimension

The disorder-driven phase transition of the RFIM is observed using exact ground-state computer simulations for hyper cubic lattices in d=5,6,7 dimensions. Finite-size scaling analyses are used to calculate the critical point and the critical exponents of the specific heat, magnetization, susceptibility and of the correlation length. For dimensions d=6,7 which are larger or equal to the assumed upper critical dimension, d_u=6, mean-field behaviour is found, i.e. alpha=0, beta=1/2, gamma=1, nu=1/2. For the analysis of the numerical data, it appears to be necessary to include recently proposed corrections to scaling at and beyond the upper critical dimension.

cond-mat.dis-nn↗

arXiv2009

Introduction to Randomness and Statistics

This text provides a practical introduction to randomness and data analysis, in particular in the context of computer simulations. At the beginning, the most basics concepts of probability are given, in particular discrete and continuous random variables. Next, generation of pseudo random numbers is covered, such as uniform generators, discrete random numbers, the inversion method, the rejection method and the Box-Mueller Method. In the third section, estimators, confidence intervals, histograms and resampling using Bootstrap are explained. Furthermore, data plotting using the freely available tools gnuplot and xmgrace is treated. In the fifth section, some foundations of hypothesis testing are given, in particular the chi-squared test, the Kolmogorov-Smirnov test and testing for statistical (in-)dependence. Finally, the maximum-likelihood principle and data fitting are explained. The text is basically self-contained, comes with several example C programs and contains eight practical (mainly programming) exercises.

physics.comp-ph↗

arXiv2009

Ultrametricity and clustering of states in spin glasses: A one-dimensional view

We present results from Monte Carlo simulations to test for ultrametricity and clustering properties in spin-glass models. By using a one-dimensional Ising spin glass with random power-law interactions where the universality class of the model can be tuned by changing the power-law exponent, we find signatures of ultrametric behavior both in the mean-field and non-mean-field universality classes for large linear system sizes. Furthermore, we confirm the existence of nontrivial connected components in phase space via a clustering analysis of configurations.

cond-mat.dis-nn↗

arXiv2008

New Insights from One-Dimensional Spin Glasses

The concept of replica symmetry breaking found in the solution of the mean-field Sherrington-Kirkpatrick spin-glass model has been applied to a variety of problems in science ranging from biological to computational and even financial analysis. Thus it is of paramount importance to understand which predictions of the mean-field solution apply to non-mean-field systems, such as realistic short-range spin-glass models. The one-dimensional spin glass with random power-law interactions promises to be an ideal test-bed to answer this question: Not only can large system sizes-which are usually a shortcoming in simulations of high-dimensional short-range system-be studied, by tuning the power-law exponent of the interactions the universality class of the model can be continuously tuned from the mean-field to the short-range universality class. We present details of the model, as well as recent applications to some questions of the physics of spin glasses. First, we study the existence of a spin-glass state in an external field. In addition, we discuss the existence of ultrametricity in short-range spin glasses. Finally, because the range of interactions can be changed, the model is a formidable test-bed for optimization algorithms.

cond-mat.dis-nn↗

arXiv2008

X-Ray Propagation in Tapered Waveguides: Simulation and Optimization

We use the parabolic wave equation to study the propagation of x-rays in tapered waveguides by numercial simulation and optimization. The goal of the study is to elucidate how beam concentration can be best achieved in x-ray optical nanostructures. Such optimized waveguides can e.g. be used to investigate single biomolecules. Here, we compare tapering geometries, which can be parametrized by linear and third-order (Bezier-type) functions and can be fabricated using standard e-beam litography units. These geometries can be described in two and four-dimensional parameter spaces, respectively. In both geometries, we observe a rugged structure of the optimization problem's ``gain landscape''. Thus, the optimization of x-ray nanostructures in general will be a highly nontrivial optimization problem.

cond-mat.mtrl-sci↗

arXiv2007

First excitations in two- and three-dimensional random-field Ising systems

We present results on the first excited states for the random-field Ising model. These are based on an exact algorithm, with which we study the excitation energies and the excitation sizes for two- and three-dimensional random-field Ising systems with a Gaussian distribution of the random fields. Our algorithm is based on an approach of Frontera and Vives which, in some cases, does not yield the true first excited states. Using the corrected algorithm, we find that the order-disorder phase transition for three dimensions is visible via crossings of the excitations-energy curves for different system sizes, while in two-dimensions these crossings converge to zero disorder. Furthermore, we obtain in three dimensions a fractal dimension of the excitations cluster of d_s=2.42(2). We also provide analytical droplet arguments to understand the behavior of the excitation energies for small and large disorder as well as close to the critical point.

cond-mat.dis-nn↗

arXiv2007

Solution-space structure of (some) optimization problems

We study numerically the cluster structure of random ensembles of two NP-hard optimization problems originating in computational complexity, the vertex-cover problem and the number partitioning problem. We use branch-and-bound type algorithms to obtain exact solutions of these problems for moderate system sizes. Using two methods, direct neighborhood-based clustering and hierarchical clustering, we investigate the structure of the solution space. The main result is that the correspondence between solution structure and the phase diagrams of the problems is not unique. Namely, for vertex cover we observe a drastic change of the solution space from large single clusters to multiple nested levels of clusters. In contrast, for the number-partitioning problem, the phase space looks always very simple, similar to a random distribution of the lowest-energy configurations. This holds in the ``easy''/solvable phase as well as in the ``hard''/unsolvable phase.

cond-mat.dis-nn↗

arXiv2007

Ground-State and Domain-Wall Energies in the Spin-Glass Region of the 2D $\pm J$ Random-Bond Ising Model

The statistics of the ground-state and domain-wall energies for the two-dimensional random-bond Ising model on square lattices with independent, identically distributed bonds of probability $p$ of $J_{ij}= -1$ and $(1-p)$ of $J_{ij}= +1$ are studied. We are able to consider large samples of up to $320^2$ spins by using sophisticated matching algorithms. We study $L \times L$ systems, but we also consider $L \times M$ samples, for different aspect ratios $R = L / M$. We find that the scaling behavior of the ground-state energy and its sample-to-sample fluctuations inside the spin-glass region ($p_c \le p \le 1 - p_c$) are characterized by simple scaling functions. In particular, the fluctuations exhibit a cusp-like singularity at $p_c$. Inside the spin-glass region the average domain-wall energy converges to a finite nonzero value as the sample size becomes infinite, holding $R$ fixed. Here, large finite-size effects are visible, which can be explained for all $p$ by a single exponent $ω\approx 2/3$, provided higher-order corrections to scaling are included. Finally, we confirm the validity of aspect-ratio scaling for $R \to 0$: the distribution of the domain-wall energies converges to a Gaussian for $R \to 0$, although the domain walls of neighboring subsystems of size $L \times L$ are not independent.

cond-mat.dis-nn↗

arXiv2006

Numerical analysis of the quantum dots on off-normal incidence ion sputtered surfaces

We implement substrate rotation in a 2+1 dimensional solid-on-solid model of ion beam sputtering of solid surfaces. With this extension of the model, we study the effect of concurrent rotation, as the surface is sputtered, on possible topographic regions of surface patterns. In particular we perform a detailed numerical analysis of the time evolution of dots obtained from our Monte Carlo simulations at off-normal-incidence sputter erosion. We found the same power-law scaling exponents of the dot characteristics for two different sets of ion-material combinations, without and with substrate rotation.

cond-mat.mtrl-sci↗

arXiv2006

RNA secondary structure design

We consider the inverse-folding problem for RNA secondary structures: for a given (pseudo-knot-free) secondary structure find a sequence that has that structure as its ground state. If such a sequence exists, the structure is called designable. We implemented a branch-and-bound algorithm that is able to do an exhaustive search within the sequence space, i.e., gives an exact answer whether such a sequence exists. The bound required by the branch-and-bound algorithm are calculated by a dynamic programming algorithm. We consider different alphabet sizes and an ensemble of random structures, which we want to design. We find that for two letters almost none of these structures are designable. The designability improves for the three-letter case, but still a significant fraction of structures is undesignable. This changes when we look at the natural four-letter case with two pairs of complementary bases: undesignable structures are the exception, although they still exist. Finally, we also study the relation between designability and the algorithmic complexity of the branch-and-bound algorithm. Within the ensemble of structures, a high average degree of undesignability is correlated to a long time to prove that a given structure is (un-)designable. In the four-letter case, where the designability is high everywhere, the algorithmic complexity is highest in the region of naturally occurring RNA.

physics.bio-ph↗

arXiv2006

What do we learn from correlations of local and global network properties?

In complex networks a common task is to identify the most important or "central" nodes. There are several definitions, often called centrality measures, which often lead to different results. Here we study extensively correlations between four local and global measures namely the degree, the shortest-path-betweenness, the random-walk betweenness and the subgraph centrality on different random-network models like Erdos-Renyi, Small-World and Barabasi-Albert as well as on different real networks like metabolic pathways, social collaborations and computer networks. Correlations are quite different between the real networks and the model networks questioning whether the models really reflect all important properties of the real world.

physics.soc-ph↗

arXiv2006

Probing tails of energy distributions using importance-sampling in the disorder with a guiding function

We propose a simple and general procedure based on a recently introduced approach that uses an importance-sampling Monte Carlo algorithm in the disorder to probe to high precision the tails of ground-state energy distributions of disordered systems. Our approach requires an estimate of the ground-state energy distribution as a guiding function which can be obtained from simple-sampling simulations. In order to illustrate the algorithm, we compute the ground-state energy distribution of the Sherrington-Kirkpatrick mean-field Ising spin glass to eighteen orders of magnitude. We find that the ground-state energy distribution in the thermodynamic limit is well fitted by a modified Gumbel distribution as previously predicted, but with a value of the slope parameter m which is clearly larger than 6 and of the order 11.

cond-mat.dis-nn↗

arXiv2006

Introduction to graphs

Graph theory provides fundamental concepts for many fields of science like statistical physics, network analysis and theoretical computer science. Here we give a pedagogical introduction to graph theory, divided into three sections. In the first, we introduce some basic notations and graph theoretical problems, e.g. Eulerian circuits, vertex covers, and graph colorings. The second section describes some fundamental algorithmic concepts to solve basic graph problems numerically, as, e.g., depth-first search, calculation of strongly connected components, and minimum-spanning tree algorithms. The last section introduces random graphs and probabilistic tools to analyze the emergence of a giant component and a giant q-core in these graphs. The presented text is published as the third chapter of the book "Phase Transitions in Combinatorial Optimization Problem" (Wiley-VCH 2005). Together with introductions to algorithms, to complexity theory and to basic statistical mechanics over random structures, it provides the technical basis for the more advanced chapters. These cover the analysis of phase transitions in combinatorial optimization problems, algorithmic and analytical approaches based on statistical physics tools (replica and cavity methods), the analysis of various search algorithms and the development of efficient heuristic algorithms, based on message passing techniques (warning, belief, and survey propagation).

cond-mat.dis-nn↗

arXiv2005

Morphological regions and oblique incidence dot formation in a model of surface sputtering

We study solid surface morphology created by off-normal ion-beam sputtering with an atomistic, solid-on-solid model of sputter erosion. With respect to an earlier version of the model, we extend this model with the inclusion of lateral erosion. Using the 2-dimensional structure factor, we found an upper bound $μ\simeq 2$, in the lateral straggle $μ$, for clear ripple formation. Above this upper bound, for longitudinal straggle $σ\gtrsim 1.7$, we found the possibility of dot formation (without sample rotation). Moreover, a temporal crossover from a hole topography to ripple topography with the same value of collision cascade parameters was found. Finally, a scaling analysis of the roughness, using the consecutive gradient approach, yields the growth exponents $β=0.33$ and 0.67 for two different topographic regimes.

cond-mat.mtrl-sci↗

arXiv2005

Propagation of Ripples in Monte Carlo Models of Sputter Induced Surface Morphology

Periodic ripples generated from the off-normal incidence ion beam bombardment of solid surfaces have been observed to propagate with a dispersion in the velocity. We investigate this ripple behaviour by means of a Monte Carlo model of the erosion process, in conjuction with one of two different surface diffusion mechanisms, representative of two different classes of materials; one is an Arrhenius-type Monte Carlo method including a term (possibly zero) that accounts for the Schwoebel effect, the other a thermodynamic mechanism without the Schwoebel effect. We find that the behavior of the ripple velocity and wavelength depends on the sputtering timescale, qualitatively consistent with experiments. Futhermore, we observe a strong temperature dependance of the ripple velocity, calling for experiments at different temperatures. Also, we observe that the ripple velocity vanishes ahead of the periodic ripple pattern.

cond-mat.mtrl-sci↗