Journal of Computer and System Sciences - 26th IEEE Conference on Foundations of Computer Science, October 21-23, 1985
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Elementary landscape decomposition of the frequency assignment problem
Theoretical Computer Science
Minimum number of below average triangles in a weighted complete graph
Discrete Optimization
Quasiabelian landscapes of the traveling salesman problem are elementary
Discrete Optimization
A parameterized runtime analysis of evolutionary algorithms for MAX-2-SAT
Proceedings of the 14th annual conference on Genetic and evolutionary computation
On distributed computation of information potentials
FOMC '12 Proceedings of the 8th International Workshop on Foundations of Mobile Computing
Quasi-elementary landscapes and superpositions of elementary landscapes
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Problem understanding through landscape theory
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Elementary landscape decomposition of the 0-1 unconstrained quadratic optimization
Journal of Heuristics
Domination analysis of algorithms for bipartite boolean quadratic programs
FCT'13 Proceedings of the 19th international conference on Fundamentals of Computation Theory
Fitness function distributions over generalized search neighborhoods in the q-ary hypercube
Evolutionary Computation
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It is shown that certain NP-complete problems (traveling salesman, min-cut graph partitioning, graph coloring, partition and a version of the satisfiability problem) satisfy a difference equation wit respect to a certain neighborhood that is similar to the wave equation of mathematical physics. Using this it is shown that any local optima with respect to these neighborhoods have a cost better than the average cost of all configuration. Also greedy local search when applied to these problems with respect to the specified neighborhoods and started from an arbitrarily poor initial configuration, will reach the average cost in at most O(nL) iterations where n is the problem size and largest cost is at most 2^L above the average cost of all configurations.