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Complexity of Linear Operators

机译:线性算子的复杂度

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Let A 0 1 n n be a matrix with z zeroes and u ones and x be an n -dimensional vector of formal variables over a semigroup ( S ) . How many semigroup operations are required to compute the linear operator Ax ?As we observe in this paper, this problem contains as a special case the well-known range queries problem and has a rich variety of applications in such areas as graph algorithms, functional programming, circuit complexity, and others. It is easy to compute Ax using O ( u ) semigroup operations. The main question studied in this paper is: can Ax be computed using O ( z ) semigroup operations? We prove that in general this is not possible: there exists a matrix A 0 1 n n with exactly two zeroes in every row (hence z = 2 n ) whose complexity is ( n ( n )) where ( n ) is the inverse Ackermann function. However, for the case when the semigroup is commutative, we give a constructive proof of an O ( z ) upper bound. This implies that in commutative settings, complements of sparse matrices can be processed as efficiently as sparse matrices (though the corresponding algorithms are more involved). Note that this covers the cases of Boolean and tropical semirings that have numerous applications, e.g., in graph theory. As a simple application of the presented linear-size construction, we show how to multiply two n n matrices over an arbitrary semiring in O ( n 2 ) time if one of these matrices is a 0/1-matrix with O ( n ) zeroes (i.e., a complement of a sparse matrix).
机译:设A 0 1 n n是具有z个零和u个的矩阵,而x是半群(S)上形式变量的n维向量。正如我们在本文中所观察到的,此问题作为特例包含众所周知的范围查询问题,并且在图算法,函数式编程等领域具有广泛的应用,这需要多少个半组运算才能计算线性算子Ax? ,电路复杂度等。使用O(u)半群运算很容易计算Ax。本文研究的主要问题是:可以使用O(z)半群运算来计算Ax吗?我们证明通常这是不可能的:存在一个矩阵A 0 1 nn,在每行中恰好有两个零(因此z = 2 n),其复杂度为(n(n)),其中(n)是逆阿克曼函数。但是,对于半群是可交换的情况,我们给出了O(z)上限的建设性证明。这意味着在可交换设置中,稀疏矩阵的补码可以像稀疏矩阵一样高效地处理(尽管相应的算法更多)。请注意,这涵盖了布尔和热带半环的情况,例如在图论中有许多应用。作为提出的线性大小构造的简单应用,我们展示了如何在O(n 2)时间内在任意半环上将两个nn矩阵相乘,如果这些矩阵之一是O(n)为零的0/1矩阵(即稀疏矩阵的补码)。

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