Chang's lemma (Duke Mathematical Journal, 2002) is a classical result with applications across several areas in mathematics and computer science. For a Boolean function $f$ that takes values in {-1,1} let $r(f)$ denote its Fourier rank. For each positive threshold $t$, Chang's lemma provides a lower bound on $wt(f):=\Pr[f(x)=-1]$ in terms of the dimension of the span of its characters with Fourier coefficients of magnitude at least $1/t$. We examine the tightness of Chang's lemma w.r.t. the following three natural settings of the threshold:
- the Fourier sparsity of $f$, denoted $k(f)$,
- the Fourier max-supp-entropy of $f$, denoted $k'(f)$, defined to be $\max \{1/|\hat{f}(S)| : \hat{f}(S) \neq 0\}$,
- the Fourier max-rank-entropy of $f$, denoted $k''(f)$, defined to be the minimum $t$ such that characters whose Fourier coefficients are at least $1/t$ in absolute value span a space of dimension $r(f)$.
We prove new lower bounds on $wt(f)$ in terms of these measures. One of our lower bounds subsumes and refines the previously best known upper bound on $r(f)$ in terms of $k(f)$ by Sanyal (ToC, 2019). Another lower bound is based on our improvement of a bound by Chattopadhyay, Hatami, Lovett and Tal (ITCS, 2019) on the sum of the absolute values of the level-$1$ Fourier coefficients. We also show that Chang's lemma for the these choices of the threshold is asymptotically outperformed by our bounds for most settings of the parameters involved.
Next, we show that our bounds are tight for a wide range of the parameters involved, by constructing functions (which are modifications of the Addressing function) witnessing their tightness. Finally we construct Boolean functions $f$ for which
- our lower bounds asymptotically match $wt(f)$, and
- for any choice of the threshold $t$, the lower bound obtained from Chang's lemma is asymptotically smaller than $wt(f)$.
VC-Dimension vs Degree: An uncertainty principle for Boolean functions
School of Statistics and Data Science, Nankai University(南开大学统计与数据科学学院);Extremal Combinatorics and Probability Group (ECOPRO), Institute for Basic Science (IBS)(基础科学研究所极值组合与概率研究组);Department of Mathematics, National University of Singapore(新加坡国立大学数学系)
The estimates for Boolean surface area of polynomial threshold functions
Nankai University(南开大学);Institute for Basic Science (IBS)(基础科学研究院);University of Washington(华盛顿大学);MSU(密歇根州立大学);Max Planck Institute for Mathematics(马普数学研究所);University of South Carolina(南卡罗来纳大学)
The Augmentation Property of Binary Matrices for the Binary and Boolean Rank
我们定义了二元矩阵关于不同秩函数的增广性质。矩阵$A$对于给定的秩函数具有增广性质,如果对于列向量$x_1,...,x_t$的任何子集,当分别用每个向量$x_i$、$1\leq i \leq t$增广时,$A$的秩不增加,则还认为当同时用所有向量$x_1,...,x_t$增广$A$时,秩不增加。这个性质对于实数上通常的线性秩来说是微不足道的,但是正如我们所展示的,当考虑矩阵的二进制和布尔秩时,情况发生了显著的变化。我们证明了一个必要和充分条件下的二元和布尔秩的二元矩阵的这一性质成立。也就是说,一个矩阵对于这些秩函数具有增广性质,当且仅当它具有一个唯一的基,该基关于给定的秩函数跨越矩阵的所有其他基。对于二进制秩,我们还提出了一个具体的特征的家庭矩阵的增广性质。这种表征是基于$V$的行之间的线性依赖关系的可能类型,在矩阵的最佳二进制分解为$A=U\cdot V$。此外,我们使用增广属性来构造简单的矩阵族,对于这些矩阵族,它们的实数秩和二进制秩之间以及它们的实数秩和布尔秩之间存在差距。