Configuration Space Analysis for Optimization Problems
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An interesting analogy between frustrated disordered systems studied in condensed matter physics and combinatorial optimization problems  has led to the use of simulated annealing (a stochastic algorithm based on the Monte Carlo method) to find approximate solutions to complex optimization problems. A common feature of these systems is the competition between objectives which favor different and incompatible types of ordering. Such “frustration” leads to the existence of a large number of nearly degenerate solutions which are not related by symmetry.
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