We give an efficient deterministic algorithm that outputs an expanding generating set for any finite abelian group. The size of the generating set is close to the randomized construction of Alon and Roichman (1994), improving upon various deterministic constructions in both the dependence on the dimension and the spectral gap. By obtaining optimal dependence on the dimension we resolve a conjecture of Azar, Motwani, and Naor (1998) in the affirmative. Our technique is an extension of the bias amplification technique of Ta-Shma (2017), who used random walks on expanders to obtain expanding generating sets over the additive group of n-bit strings. As a consequence, we obtain (i) randomness-efficient constructions of almost k-wise independent variables, (ii) a faster deterministic algorithm for the Remote Point Problem, (iii) randomness-efficient low-degree tests, and (iv) randomness-efficient verification of matrix multiplication.
翻译:我们给出了一种高效的确定算法,为任何有限的亚伯里安群体输出一个扩大生产组。 生成组的规模接近于阿隆和罗希曼的随机结构(1994年),在依赖维度和光谱差距方面改进了各种确定性结构。 通过获得对维度的最佳依赖,我们解决了Azar、Motwani和Naor(1998年)的假设。我们的技术是Ta- Shma(2017年)的偏差放大技术的延伸,Ta- Shma(2017年)在扩张器上随机散步,以获得比n-bit 字符的添加组更多的产生组。结果,我们得到了(一) 近k-wise独立变量的随机高效构造,(二) 远程点问题的快速确定性算法,(三) 随机性高效低度测试,以及(四) 矩阵倍增的随机性核查。