N Ways to Simulate Short-Range Particle Systems: Automated Algorithm Selection with the Node-Level Library AutoPas

2021 ◽  
pp. 108262
Author(s):  
Fabio Alexander Gratl ◽  
Steffen Seckler ◽  
Hans-Joachim Bungartz ◽  
Philipp Neumann
Keyword(s):  
Short Range ◽  
Particle Systems ◽  
Algorithm Selection ◽  
Automated Algorithm ◽  
Node Level

Diffusion processes in brownian particle systems with short-range interaction

1991 ◽  
Vol 74 (3) ◽  
pp. 649-664 ◽  
Author(s):  
Jörn Sonnenburg ◽  
Dietrich Kremp ◽  
Rainer Sändig
Keyword(s):  
Short Range ◽  
Diffusion Processes ◽  
Brownian Particle ◽  
Particle Systems ◽  
Short Range Interaction ◽  
Range Interaction

On the potential of normalized TSP features for automated algorithm selection

2021 ◽  
Author(s):  
Jonathan Heins ◽  
Jakob Bossek ◽  
Janina Pohl ◽  
Moritz Seiler ◽  
Heike Trautmann ◽  
...  
Keyword(s):  
Algorithm Selection ◽  
Automated Algorithm

Simplified Treatment for Strong Short-Range Repulsions inN-Particle Systems. I. General Theory

1959 ◽  
Vol 113 (2) ◽  
pp. 388-399 ◽  
Author(s):  
John W. Clark ◽  
Eugene Feenberg
Keyword(s):  
General Theory ◽  
Short Range ◽  
Particle Systems

An Avoidance Model for Short-Range Order Induced by Soft Repulsions in Systems of Rigid Rods

1996 ◽  
Vol 463 ◽  
Author(s):  
Jining Han ◽  
Judith Herzfeld
Keyword(s):  
Short Range ◽  
Short Range Order ◽  
Mean Field ◽  
Axial Ratio ◽  
Field Approximation ◽  
Particle Systems ◽  
Range Order ◽  
Mean Field Approximation ◽  
Hard Particle ◽  
Rigid Rods

ABSTRACTThe effects of soft repulsions on hard particle systems are calculated using an avoidance model which improves upon the simple mean field approximation. The method not only yields a better free energy, but also gives an estimate for the short-range positional order induced by soft repulsions. The results indicate little short-range order for isotropically oriented rods. However, for parallel rods short-range order increases to significant levels as the particle axial ratio increases.

scholarly journals Automated Algorithm Selection: Survey and Perspectives

2019 ◽  
Vol 27 (1) ◽  
pp. 3-45 ◽  
Author(s):  
Pascal Kerschke ◽  
Holger H. Hoos ◽  
Frank Neumann ◽  
Heike Trautmann
Keyword(s):  
State Of The Art ◽  
Combinatorial Problems ◽  
The State ◽  
Problem Instance ◽  
Ai Planning ◽  
Algorithm Selection ◽  
Automated Algorithm ◽  
Continuous Problems ◽  
Algorithm Configuration ◽  
The One

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

scholarly journals Controlling the Short-Range Order and Packing Densities of Many-Particle Systems

2002 ◽  
Vol 106 (43) ◽  
pp. 11406-11406 ◽  
Author(s):  
S. Torquato ◽  
F. H. Stillinger
Keyword(s):  
Short Range ◽  
Short Range Order ◽  
Particle Systems ◽  
Range Order ◽  
Packing Densities

A preprocessing and automated algorithm selection system for image registration

2006 ◽  
Author(s):  
A. L. Drozd ◽  
A. C. Blackburn ◽  
I. P. Kasperovich ◽  
P. K. Varshney ◽  
M. Xu ◽  
...  
Keyword(s):  
Image Registration ◽  
Selection System ◽  
Algorithm Selection ◽  
Automated Algorithm

scholarly journals AutoFolio: An Automatically Configured Algorithm Selector

2015 ◽  
Vol 53 ◽  
pp. 745-778 ◽  
Author(s):  
Marius Lindauer ◽  
Holger H. Hoos ◽  
Frank Hutter ◽  
Torsten Schaub
Keyword(s):  
State Of The Art ◽  
Machine Learning Techniques ◽  
Problem Instance ◽  
Algorithm Selection ◽  
The Core ◽  
Automated Algorithm ◽  
Learning Techniques ◽  
Algorithm Configuration ◽  
Optimal Values ◽  
The One

Algorithm selection (AS) techniques -- which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently -- have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. This holds specifically for the machine learning techniques that form the core of current AS procedures, and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single, highly-parameterized algorithm framework. Our approach, dubbed AutoFolio, allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods. We demonstrate AutoFolio can significantly improve the performance of claspfolio 2 on 8 out of the 13 scenarios from the Algorithm Selection Library, leads to new state-of-the-art algorithm selectors for 7 of these scenarios, and matches state-of-the-art performance (statistically) on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieves average speedup factors between 1.3 and 15.4.

scholarly journals confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms

2020 ◽  
pp. 80-95
Author(s):  
Matthias Carnein ◽  
Heike Trautmann ◽  
Albert Bifet ◽  
Bernhard Pfahringer
Keyword(s):  
Clustering Algorithms ◽  
Algorithm Selection ◽  
Stream Clustering ◽  
Automated Algorithm

scholarly journals Auto-CaseRec: Automatically Selecting and Optimizing Recommendation-Systems Algorithms

2020 ◽  
Author(s):  
Srijan Gupta ◽  
Joeran Beel
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Parameter Tuning ◽  
Machine Learning Algorithms ◽  
Algorithm Selection ◽  
Automated Algorithm ◽  
Recommendation Algorithms ◽  
Automated Machine Learning ◽  
Human Effort

The advances in the field of Automated Machine Learning (AutoML) have greatly reduced human effort in selecting and optimizing machine learning algorithms. These advances, however, have not yet widely made it to Recommender-Systems libraries. We introduce Auto-CaseRec, a Python framework based on the CaseRec recommender-system library. Auto-CaseRec provides automated algorithm selection and parameter tuning for recommendation algorithms. An initial evaluation of Auto-CaseRec against the baselines shows an average 13.88% improvement in RMSE for theMovielens100K dataset and an average 17.95% improvement in RMSE for the Last.fm dataset.

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