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On Extracting Randomness From Weak Random Sources

机译:从弱随机源中提取随机性

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We prove an exponential lower bound on the size of any fixed-degree algebraic decision tree for solving MAX, the problem of finding the maximum of n real numbers. This complements the n?1 lower bound of Rabin cite{R72} on the depth of algebraic decision trees for this problem. The proof in fact gives an exponential lower bound on size for the polyhedral decision problem MAX= of testing whether the j-th number is the maximum among a list of n real numbers. Previously, except for linear decision trees, no nontrivial lower bounds on the size of algebraic decision trees for any familiar problems are known. We also establish an interesting connection between our lower bound and the maximum number of minimal cutsets for any rank-d hypergraphs on n vertices.
机译:我们证明了求解MAX的任何固定度数代数决策树的大小的指数下界,即发现n个实数最大值的问题。对于该问题,这在代数决策树的深度上补充了Rabin cite {R72}的n?1下界。实际上,该证明为多面体决策问题MAX =的大小提供了指数下限,该问题用于测试第n个实数列表中的第j个数是否最大。以前,除了线性决策树外,对于任何熟悉的问题,代数决策树的大小没有非平凡的下界是已知的。我们还为n个顶点上任何等级d的超图的下限和最小割集的最大数目之间建立了有趣的联系。

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