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A tantalizing conjecture in discrete mathematics is the one of Komlos, suggesting that for any vectors $mathbf{a}_1,ldots,mathbf{a}_n in B_2^m$ there exist signs $x_1, dots, x_n in { -1,1}$ so that $|sum_{i=1}^n x_imathbf{a}_i|_infty le O(1)$. It is a natural extension to ask what $ell_q$-norm bound to expect for $mathbf{a}_1,ldots,mathbf{a}_n in B_p^m$. We prove that, for $2 le p le q le infty$, such vectors admit fractional colorings $x_1, dots, x_n in [-1,1]$ with a linear number of $pm 1$ coordinates so that $|sum_{i=1}^n x_imathbf{a}_i|_q leq O(sqrt{min(p,log(2m/n))}) cdot n^{1/2-1/p+ 1/q}$, and that one can obtain a full coloring at the expense of another factor of $frac{1}{1/2 - 1/p + 1/q}$. In particular, for $p in (2,3]$ we can indeed find signs $mathbf{x} in { -1,1}^n$ with $|sum_{i=1}^n x_imathbf{a}_i|_infty le O(n^{1/2-1/p} cdot frac{1}{p-2})$. Our result generalizes Spencers theorem, for which $p = q = infty$, and is tight for $m = n$. Additionally, we prove that for any fixed constant $delta>0$, in a centrally symmetric body $K subseteq mathbb{R}^n$ with measure at least $e^{-delta n}$ one can find such a fractional coloring in polynomial time. Previously this was known only for a small enough constant -- indeed in this regime classical nonconstructive arguments do not apply and partial colorings of the form $mathbf{x} in { -1,0,1}^n$ do not necessarily exist.
We study the online discrepancy minimization problem for vectors in $mathbb{R}^d$ in the oblivious setting where an adversary is allowed fix the vectors $x_1, x_2, ldots, x_n$ in arbitrary order ahead of time. We give an algorithm that maintains $O(s
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This paper concerns the universal approximation property with neural networks in variable Lebesgue spaces. We show that, whenever the exponent function of the space is bounded, every function can be approximated with shallow neural networks with any