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Jackson's inequality on the hypercube

Paata Ivanisvili, Roman Vershynin, Xinyuan Xie

Abstract

We investigate the best constant $J(n,d)$ such that Jackson's inequality \[ \inf_{\mathrm{deg}(g) \leq d} \|f - g\|_{\infty} \leq J(n,d) \, s(f), \] holds for all functions $f$ on the hypercube $\{0,1\}^n$, where $s(f)$ denotes the sensitivity of $f$. We show that the quantity $J(n, 0.499n)$ is bounded below by an absolute positive constant, independent of $n$. This complements Wagner's theorem, which establishes that $J(n,d)\leq 1 $. As a first application we show that reverse Bernstein inequality fails in the tail space $L^{1}_{\geq 0.499n}$ improving over previously known counterexamples in $L^{1}_{\geq C \log \log (n)}$. As a second application, we show that there exists a function $f : \{0,1\}^n \to [-1,1]$ whose sensitivity $s(f)$ remains constant, independent of $n$, while the approximate degree grows linearly with $n$. This result implies that the sensitivity theorem $s(f) \geq Ω(\mathrm{deg}(f)^C)$ fails in the strongest sense for bounded real-valued functions even when $\mathrm{deg}(f)$ is relaxed to the approximate degree. We also show that in the regime $d = (1 - δ)n$, the bound \[ J(n,d) \leq C \min\{δ, \max\{δ^2, n^{-2/3}\}\} \] holds. Moreover, when restricted to symmetric real-valued functions, we obtain $J_{\mathrm{symmetric}}(n,d) \leq C/d$ and the decay $1/d$ is sharp. Finally, we present results for a subspace approximation problem: we show that there exists a subspace $E$ of dimension $2^{n-1}$ such that $\inf_{g \in E} \|f - g\|_{\infty} \leq s(f)/n$ holds for all $f$.

Jackson's inequality on the hypercube

Abstract

We investigate the best constant such that Jackson's inequality holds for all functions on the hypercube , where denotes the sensitivity of . We show that the quantity is bounded below by an absolute positive constant, independent of . This complements Wagner's theorem, which establishes that . As a first application we show that reverse Bernstein inequality fails in the tail space improving over previously known counterexamples in . As a second application, we show that there exists a function whose sensitivity remains constant, independent of , while the approximate degree grows linearly with . This result implies that the sensitivity theorem fails in the strongest sense for bounded real-valued functions even when is relaxed to the approximate degree. We also show that in the regime , the bound holds. Moreover, when restricted to symmetric real-valued functions, we obtain and the decay is sharp. Finally, we present results for a subspace approximation problem: we show that there exists a subspace of dimension such that holds for all .

Paper Structure

This paper contains 21 sections, 19 theorems, 96 equations.

Key Result

Proposition 1

There exists a universal constant $C>0$ such that for any symmetric function $f:\{0,1\}^{n} \to \mathbb{R}$ and any $d \in (0,n]$, we have Moreover, this bound is optimal. For every $n \in \mathbb{N}$ and any $d \in (0,n]$, there exists a symmetric function $f:\{0,1\}^{n} \to \mathbb{R}$ with $s(f) > 0$ such that

Theorems & Definitions (24)

  • Proposition 1: Approximability of symmetric functions by low-degree polynomials
  • Theorem 1: Inapproximability of general functions by low-degree polynomials
  • Corollary 2: No sensitivity bound for real-valued functions via approximate degree
  • Corollary 3
  • Theorem 4: Inapproximability by low-dimensional subspaces
  • Theorem 5: Approximability by a subspace of half-dimension
  • Theorem 6
  • Corollary 7
  • Corollary 8
  • Corollary 9: Approximation by high-degree polynomials
  • ...and 14 more