Assume we are interested in solving a computational task, e.g., sorting n numbers, and we only have access to an unreliable primitive operation, for example, comparison between two numbers. Suppose that each primitive operation fails with probability at most p and that repeating it is not helpful, as it will result in the same outcome. Can we still approximately solve our task with probability 1-f(p) for a function f that goes to 0 as p goes to 0? While previous work studied sorting in this model, we believe this model is also relevant for other problems. We find the maximum of n numbers in O(n) time, solve 2D linear programming in O(n log n) time, approximately sort n numbers in O(n 2) time such that each number's position deviates from its true rank by at most O(logn) positions, find an element in a sorted array in O(log n log log n) time. Our sorting result can be seen as an alternative to a previous result of Braverman and Mossel (SODA, 2008) who employed the same model. While we do not construct the maximum likelihood permutation, we achieve similar accuracy with a substantially faster running time. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Klein, R., Penninger, R., Sohler, C., & Woodruff, D. P. (2011). Tolerant algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6942 LNCS, pp. 736–747). https://doi.org/10.1007/978-3-642-23719-5_62
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