
The interval greedy algorithm for discrete optimization problems with interval objective function
We consider the discrete optimization problems with interval objective f...
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The discrete optimization problems with interval objective function on graphs and hypergraphs and the interval greedy algorithm
We consider the discrete optimization problems with interval objective f...
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A tabu search algorithm with efficient diversification strategy for high school timetabling problem
The school timetabling problem can be described as scheduling a set of l...
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Chebushev Greedy Algorithm in convex optimization
Chebyshev Greedy Algorithm is a generalization of the well known Orthogo...
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Biorthogonal greedy algorithms in convex optimization
The study of greedy approximation in the context of convex optimization ...
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Iteratively reweighted greedy set cover
We empirically analyze a simple heuristic for large sparse set cover pro...
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Greedy Graph Searching for Vascular Tracking in Angiographic Image Sequences
Vascular tracking of angiographic image sequences is one of the most cli...
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Squeaky Wheel Optimization
We describe a general approach to optimization which we term `Squeaky Wheel' Optimization (SWO). In SWO, a greedy algorithm is used to construct a solution which is then analyzed to find the trouble spots, i.e., those elements, that, if improved, are likely to improve the objective function score. The results of the analysis are used to generate new priorities that determine the order in which the greedy algorithm constructs the next solution. This Construct/Analyze/Prioritize cycle continues until some limit is reached, or an acceptable solution is found. SWO can be viewed as operating on two search spaces: solutions and prioritizations. Successive solutions are only indirectly related, via the reprioritization that results from analyzing the prior solution. Similarly, successive prioritizations are generated by constructing and analyzing solutions. This `coupled search' has some interesting properties, which we discuss. We report encouraging experimental results on two domains, scheduling problems that arise in fiberoptic cable manufacturing, and graph coloring problems. The fact that these domains are very different supports our claim that SWO is a general technique for optimization.
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