Statistical mechanics methods and phase transitions in optimization problems

Recently, it has been recognized that phase transitions play an important role in the probabilistic analysis of combinatorial optimization problems. However, there are in fact many other relations that lead to close ties between computer science and statistical physics. This review aims at presenting the tools and concepts designed by physicists to deal with optimization or decision problems in...

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2 00 1 Statistical mechanics methods and phase transitions in optimization problems

Recently, it has been recognized that phase transitions play an important role in the probabilistic analysis of combinatorial optimization problems. However, there are in fact many other relations that lead to close ties between computer science and statistical physics. This review aims at presenting the tools and concepts designed by physicists to deal with optimization or decision problems in...

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Article on Statistical Mechanics Methods and Phase Transitions in Optimization Problems

Theoretical methods from statistical physics can be used in optimization problems in computer science and mathematics, as they often be defined by an energy-function which needs to minimized to find the optimal solution. These methods can be used to gain information about statistical properties of these systems, and can aid in finding the lowest energy configuration (i.e. optimization). In addi...

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2 00 1 Statistical mechanics methods and phase transitions in optimization problems . Olivier

Recently, it has been recognized that phase transitions play an important role in the probabilistic analysis of combinatorial optimization problems. However, there are in fact many other relations that lead to close ties between computer science and statistical physics. This review aims at presenting the tools and concepts designed by physicists to deal with optimization or decision problems in...

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Phase Transitions in Parameter Rich Optimization Problems

Most real world combinatorial optimization problems are affected by noise in the input data. Search algorithms to identify “good” solutions with low costs behave like the dynamics of large disordered particle systems, e.g., random networks or spin glasses. Such solutions to noise perturbed optimization problems are characterized by Gibbs distributions when the optimization algorithm searches fo...

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Statistical mechanics methods and phase transitions in optimization problems

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