TY - JOUR
T1 - Stigmatized Portrayals of Single Women
T2 - A Content Analysis of News Coverage on Single Women and Single Men in China
AU - Gong, Wanqi
AU - Tu, Caixie
AU - Jiang, L. Crystal
PY - 2017
Y1 - 2017
N2 - We study the average-case complexity of mm-sum product of matrices, which is a fundamental operation that has many applications in computer science. We focus on optimizing the number of "algebraic" operations (i.e., operations involving real numbers) used in the computation, since such operations are usually expensive in various environments. We present an algorithm that can compute the min-sum product of two n x n real matrices using only O(n(2)) algebraic operations, given that the matrix elements are drawn independently and identically from some fixed probability distribution satisfying several constraints. This improves the previously best known upper-bound of O(n(2) logo). The class of probability distributions under which our algorithm works include many important and commonly used distributions, such as uniform distributions, exponential distributions, folded normal distributions, etc. In order to evaluate the performance of the proposed algorithm, we performed experiments to compare the running time of the proposed algorithm with algorithms in [1]. The experimental results demonstrate that our algorithm achieves significant performance improvement over the previous algorithms. (C) 2015 Elsevier B.V. All rights reserved.
AB - We study the average-case complexity of mm-sum product of matrices, which is a fundamental operation that has many applications in computer science. We focus on optimizing the number of "algebraic" operations (i.e., operations involving real numbers) used in the computation, since such operations are usually expensive in various environments. We present an algorithm that can compute the min-sum product of two n x n real matrices using only O(n(2)) algebraic operations, given that the matrix elements are drawn independently and identically from some fixed probability distribution satisfying several constraints. This improves the previously best known upper-bound of O(n(2) logo). The class of probability distributions under which our algorithm works include many important and commonly used distributions, such as uniform distributions, exponential distributions, folded normal distributions, etc. In order to evaluate the performance of the proposed algorithm, we performed experiments to compare the running time of the proposed algorithm with algorithms in [1]. The experimental results demonstrate that our algorithm achieves significant performance improvement over the previous algorithms. (C) 2015 Elsevier B.V. All rights reserved.
UR - http://www.scopus.com/inward/record.url?scp=84945232714&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84945232714&origin=recordpage
U2 - 10.1080/09589236.2015.1095082
DO - 10.1080/09589236.2015.1095082
M3 - 21_Publication in refereed journal
VL - 26
SP - 197
EP - 211
JO - Journal of Gender Studies
JF - Journal of Gender Studies
SN - 0958-9236
IS - 2
ER -