No Arabic abstract
The purpose of this paper is to extend the result of arXiv:1810.00823 to mixed Holder functions on $[0,1]^d$ for all $d ge 1$. In particular, we prove that by sampling an $alpha$-mixed Holder function $f : [0,1]^d rightarrow mathbb{R}$ at $sim frac{1}{varepsilon} left(log frac{1}{varepsilon} right)^d$ independent uniformly random points from $[0,1]^d$, we can construct an approximation $tilde{f}$ such that $$ |f - tilde{f}|_{L^2} lesssim varepsilon^alpha left(log textstyle{frac{1}{varepsilon}} right)^{d-1/2}, $$ with high probability.
Suppose $f : [0,1]^2 rightarrow mathbb{R}$ is a $(c,alpha)$-mixed Holder function that we sample at $l$ points $X_1,ldots,X_l$ chosen uniformly at random from the unit square. Let the location of these points and the function values $f(X_1),ldots,f(X_l)$ be given. If $l ge c_1 n log^2 n$, then we can compute an approximation $tilde{f}$ such that $$ |f - tilde{f} |_{L^2} = mathcal{O}(n^{-alpha} log^{3/2} n), $$ with probability at least $1 - n^{2 -c_1}$, where the implicit constant only depends on the constants $c > 0$ and $c_1 > 0$.
Let h_R denote an L ^{infty} normalized Haar function adapted to a dyadic rectangle R contained in the unit cube in dimension d. We establish a non-trivial lower bound on the L^{infty} norm of the `hyperbolic sums $$ sum _{|R|=2 ^{-n}} alpha(R) h_R (x) $$ The lower bound is non-trivial in that we improve the average case bound by n^{eta} for some positive eta, a function of dimension d. As far as the authors know, this is the first result of this type in dimension 4 and higher. This question is related to Conjectures in (1) Irregularity of Distributions, (2) Approximation Theory and (3) Probability Theory. The method of proof of this paper gives new results on these conjectures in all dimensions 4 and higher. This paper builds upon prior work of Jozef Beck, from 1989, and first two authors from 2006. These results were of the same nature, but only in dimension 3.
We adapt Guths polynomial partitioning argument for the Fourier restriction problem to the context of the Kakeya problem. By writing out the induction argument as a recursive algorithm, additional multiscale geometric information is made available. To take advantage of this, we prove that direction-separated tubes satisfy a multiscale version of the polynomial Wolff axioms. Altogether, this yields improved bounds for the Kakeya maximal conjecture in $mathbb{R}^n$ with $n=5$ or $nge 7$ and improved bounds for the Kakeya set conjecture for an infinite sequence of dimensions.
A symmetrization inequality of Rogers and of Brascamp-Lieb-Luttinger states that for a certain class of multilinear integral expressions, among tuples of sets of prescribed Lebesgue measures, tuples of balls centered at the origin are among the maximizers. Under natural hypotheses, we characterize all maximizing tuples for these inequalities for dimensions strictly greater than 1. We establish a sharpened form of the inequality.
Stochastic processes are proposed whose master equations coincide with classical wave, telegraph, and Klein-Gordon equations. Similar to predecessors based on the Goldstein-Kac telegraph process, the model describes the motion of particles with constant speed and transitions between discreet allowed velocity directions. A new ingredient is that transitions into a given velocity state depend on spatial derivatives of other states populations, rather than on populations themselves. This feature requires the sacrifice of the single-particle character of the model, but allows to imitate the Huygens principle and to recover wave equations in arbitrary dimensions.