A decomposition method for quadratic zero-one programming
Management Science
Correlation length, isotropy and meta-stable states
Proceedings of the 16th annual international conference of the Center for Nonlinear Studies on Landscape paradigms in physics and biology : concepts, structures and dynamics: concepts, structures and dynamics
Autocorrelation coefficient for the graph bipartitioning problem
Theoretical Computer Science
Solving quadratic (0,1)-problems by semidefinite programs and cutting planes
Mathematical Programming: Series A and B
A scatter search approach to unconstrained quadratic binary programs
New ideas in optimization
On the classification of NP-complete problems in terms of their correlation coefficient
Proceedings of the 5th Twente workshop on on Graphs and combinatorial optimization
On the landscape ruggedness of the quadratic assignment problem
Theoretical Computer Science
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming
Journal of Heuristics
Understanding elementary landscapes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A polynomial time computation of the exact correlation structure of k-satisfiability landscapes
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Applying Elementary Landscape Analysis to Search-Based Software Engineering
SSBSE '10 Proceedings of the 2nd International Symposium on Search Based Software Engineering
Local search and the local structure of NP-complete problems
Operations Research Letters
Problem understanding through landscape theory
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Landscapes' theory provides a formal framework in which combinatorial optimization problems can be theoretically characterized as a sum of an especial kind of landscape called elementary landscape. The elementary landscape decomposition of a combinatorial optimization problem is a useful tool for understanding the problem. Such decomposition provides an additional knowledge on the problem that can be exploited to explain the behavior of some existing algorithms when they are applied to the problem or to create new search methods for the problem. In this paper we analyze the 0-1 Unconstrained Quadratic Optimization from the point of view of landscapes' theory. We prove that the problem can be written as the sum of two elementary components and we give the exact expressions for these components. We use the landscape decomposition to compute autocorrelation measures of the problem, and show some practical applications of the decomposition.