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ABSTRACT
We study the question of what optimization problems can be optimally or approximately solved by "greedy-like" algorithms. For definiteness, we will limit the present discussion to some well-studied scheduling problems although the underlying issues apply in a much more general setting. Of course, the main benefit of greedy algorithms lies in both their conceptual simplicity and their computational efficiency. Based on the experience from online competitive analysis, it seems plausible that we should be able to derive approximation bounds for "greedy-like" algorithms exploiting only the conceptual simplicity of these algorithms. To this end, we will provide a precise definition of what we mean by greedy and greedy-like. A full version of this paper is available at http://www.cs.toronto.edu/~ bor/priority.ps.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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