Chapter 22. Connections to Statistical Physics

2021 ◽  
Author(s):  
Fabrizio Altarelli ◽  
Rémi Monasson ◽  
Guilhem Semerjian ◽  
Francesco Zamponi
Keyword(s):  
Phase Transitions ◽  
Low Temperature ◽  
Statistical Mechanics ◽  
Cost Function ◽  
Computer Science ◽  
Statistical Physics ◽  
Energy Landscape ◽  
Research Activity ◽  
Detailed Picture ◽  
Intense Research

This chapter surveys a part of the intense research activity that has been devoted by theoretical physicists to the study of randomly generated k-SAT instances. It can be at first sight surprising that there is a connection between physics and computer science. However low-temperature statistical mechanics concerns precisely the behaviour of the low-lying configurations of an energy landscape, in other words the optimization of a cost function. Moreover the ensemble of random k-SAT instances exhibit phase transitions, a phenomenon mostly studied in physics (think for instance at the transition between liquid and gaseous water). Besides the introduction of general concepts of statistical mechanics and their translations in computer science language, the chapter presents results on the location of the satisfiability transition, the detailed picture of the satisfiable regime and the various phase transitions it undergoes, and algorithmic issues for random k-SAT instances.

Chapter 10. Random Satisfiabiliy

2021 ◽  
Author(s):  
Dimitris Achlioptas
Keyword(s):  
Statistical Physics ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Generative Models ◽  
Research Activity ◽  
Mathematical Properties ◽  
Satisfiability Algorithms ◽  
Resolution Proof ◽  
Intense Research ◽  
Cnf Formulas

In the last twenty years a significant amount of effort has been devoted to the study of randomly generated satisfiability instances. While a number of generative models have been proposed, uniformly random k-CNF formulas are by now the dominant and most studied model. One reason for this is that such formulas enjoy a number of intriguing mathematical properties, including the following: for each k≥3, there is a critical value, rk, of the clauses-to-variables ratio, r, such that for r<rk a random k-CNF formula is satisfiable with probability that tends to 1 as n→∞, while for r>rk it is unsatisfiable with probability that tends to 1 as n→∞. Algorithmically, even at densities much below rk, no polynomial-time algorithm is known that can find any solution even with constant probability, while for all densities greater than rk, the length of every resolution proof of unsatisfiability is exponential (and, thus, so is the running time of every DPLL-type algorithm). By now, the study of random k-CNF formulas has also attracted attention in areas such as mathematics and statistical physics and is at the center of an area of intense research activity. At the same time, random k-SAT instances are a popular benchmark for testing and tuning satisfiability algorithms. Indeed, some of the better practical ideas in use today come from insights gained by studying the performance of algorithms on them. We review old and recent mathematical results about random k-CNF formulas, demonstrating that the connection between computational complexity and phase transitions is both deep and highly nuanced.

Computational Complexity and Statistical Physics

2005 ◽  
Keyword(s):  
Phase Transitions ◽  
Computational Complexity ◽  
Computer Science ◽  
Statistical Physics ◽  
Combinatorial Problems ◽  
Complete Problems ◽  
Computer Scientists ◽  
Background Material ◽  
Np Complete ◽  
Exciting Area

Computer science and physics have been closely linked since the birth of modern computing. In recent years, an interdisciplinary area has blossomed at the junction of these fields, connecting insights from statistical physics with basic computational challenges. Researchers have successfully applied techniques from the study of phase transitions to analyze NP-complete problems such as satisfiability and graph coloring. This is leading to a new understanding of the structure of these problems, and of how algorithms perform on them. Computational Complexity and Statistical Physics will serve as a standard reference and pedagogical aid to statistical physics methods in computer science, with a particular focus on phase transitions in combinatorial problems. Addressed to a broad range of readers, the book includes substantial background material along with current research by leading computer scientists, mathematicians, and physicists. It will prepare students and researchers from all of these fields to contribute to this exciting area.

Introduction

2017 ◽  
Author(s):  
Jochen Rau
Keyword(s):  
Probability Theory ◽  
Phase Transitions ◽  
Statistical Mechanics ◽  
Conservation Laws ◽  
Law Of Large Numbers ◽  
Macroscopic Scale ◽  
Classical Probability ◽  
Large Numbers ◽  
Microscopic World ◽  
New Phenomena

Statistical mechanics concerns the transition from the microscopic to the macroscopic realm. On a macroscopic scale new phenomena arise that have no counterpart in the microscopic world. For example, macroscopic systems have a temperature; they might undergo phase transitions; and their dynamics may involve dissipation. How can such phenomena be explained? This chapter discusses the characteristic differences between the microscopic and macroscopic realms and lays out the basic challenge of statistical mechanics. It suggests how, in principle, this challenge can be tackled with the help of conservation laws and statistics. The chapter reviews some basic notions of classical probability theory. In particular, it discusses the law of large numbers and illustrates how, despite the indeterminacy of individual events, statistics can make highly accurate predictions about totals and averages.

Statistical physics and phase transitions

2012 ◽  
pp. 12-42
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Phase Transitions ◽  
Statistical Physics

X-ray study of the low-temperature phase transitions in LiKSO4

1983 ◽  
Vol 4 (1) ◽  
pp. 37-45 ◽  
Author(s):  
P. E. Tomaszewski ◽  
K. Łukaszewicz
Keyword(s):  
Phase Transitions ◽  
Low Temperature ◽  
Temperature Phase ◽  
X Ray ◽  
Low Temperature Phase

scholarly journals An energy landscape approach to locomotor transitions in complex 3D terrain

2020 ◽  
Vol 117 (26) ◽  
pp. 14987-14995 ◽  
Author(s):  
Ratan Othayoth ◽  
George Thoms ◽  
Chen Li
Keyword(s):  
Potential Energy ◽  
Complex Terrain ◽  
Statistical Physics ◽  
Energy Landscape ◽  
Three Dimensional ◽  
Single Mode ◽  
Physical Interaction ◽  
Landscape Approach ◽  
3D Terrain ◽  
Potential Energy Landscape

Effective locomotion in nature happens by transitioning across multiple modes (e.g., walk, run, climb). Despite this, far more mechanistic understanding of terrestrial locomotion has been on how to generate and stabilize around near–steady-state movement in a single mode. We still know little about how locomotor transitions emerge from physical interaction with complex terrain. Consequently, robots largely rely on geometric maps to avoid obstacles, not traverse them. Recent studies revealed that locomotor transitions in complex three-dimensional (3D) terrain occur probabilistically via multiple pathways. Here, we show that an energy landscape approach elucidates the underlying physical principles. We discovered that locomotor transitions of animals and robots self-propelled through complex 3D terrain correspond to barrier-crossing transitions on a potential energy landscape. Locomotor modes are attracted to landscape basins separated by potential energy barriers. Kinetic energy fluctuation from oscillatory self-propulsion helps the system stochastically escape from one basin and reach another to make transitions. Escape is more likely toward lower barrier direction. These principles are surprisingly similar to those of near-equilibrium, microscopic systems. Analogous to free-energy landscapes for multipathway protein folding transitions, our energy landscape approach from first principles is the beginning of a statistical physics theory of multipathway locomotor transitions in complex terrain. This will not only help understand how the organization of animal behavior emerges from multiscale interactions between their neural and mechanical systems and the physical environment, but also guide robot design, control, and planning over the large, intractable locomotor-terrain parameter space to generate robust locomotor transitions through the real world.

Low temperature microstructure and phase transitions in La2−xSrxCuO4 and La2−xBaxCuO4

1991 ◽  
Vol 175 (3-4) ◽  
pp. 301-309 ◽  
Author(s):  
C.H. Chen ◽  
S-W. Cheong ◽  
D.J. Werder ◽  
A.S. Cooper ◽  
L.W. Rupp
Keyword(s):  
Phase Transitions ◽  
Low Temperature
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