No Arabic abstract
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 $O(sqrt{log(nd/delta)})$ discrepancy with probability $1-delta$, matching the lower bound given in [Bansal et al. 2020] up to an $O(sqrt{log log n})$ factor in the high-probability regime. We also provide results for the weighted and multi-col
We consider the {em vector partition problem}, where $n$ agents, each with a $d$-dimensional attribute vector, are to be partitioned into $p$ parts so as to minimize cost which is a given function on the sums of attribute vectors in each part. The problem has applications in a variety of areas including clustering, logistics and health care. We consider the complexity and parameterized complexity of the problem under various assumptions on the natural parameters $p,d,a,t$ of the problem where $a$ is the maximum absolute value of any attribute and $t$ is the number of agent types, and raise some of the many remaining open problems.
We introduce and study finite $d$-volumes - the high dimensional generalization of finite metric spaces. Having developed a suitable combinatorial machinery, we define $ell_1$-volumes and show that they contain Euclidean volumes and hypertree volumes. We show that they can approximate any $d$-volume with $O(n^d)$ multiplicative distortion. On the other hand, contrary to Bourgains theorem for $d=1$, there exists a $2$-volume that on $n$ vertices that cannot be approximated by any $ell_1$-volume with distortion smaller than $tilde{Omega}(n^{1/5})$. We further address the problem of $ell_1$-dimension reduction in the context of $ell_1$ volumes, and show that this phenomenon does occur, although not to the same striking degree as it does for Euclidean metrics and volumes. In particular, we show that any $ell_1$ metric on $n$ points can be $(1+ epsilon)$-approximated by a sum of $O(n/epsilon^2)$ cut metrics, improving over the best previously known bound of $O(n log n)$ due to Schechtman. In order to deal with dimension reduction, we extend the techniques and ideas introduced by Karger and Bencz{u}r, and Spielman et al.~in the context of graph Sparsification, and develop general methods with a wide range of applications.
Perturbed graphic matroids are binary matroids that can be obtained from a graphic matroid by adding a noise of small rank. More precisely, r-rank perturbed graphic matroid M is a binary matroid that can be represented in the form I +P, where I is the incidence matrix of some graph and P is a binary matrix of rank at most r. Such matroids naturally appear in a number of theoretical and applied settings. The main motivation behind our work is an attempt to understand which parameterized algorithms for various problems on graphs could be lifted to perturbed graphic matroids. We study the parameterized complexity of a natural generalization (for matroids) of the following fundamental problems on graphs: Steiner Tree and Multiway Cut. In this generalization, called the Space Cover problem, we are given a binary matroid M with a ground set E, a set of terminals Tsubseteq E, and a non-negative integer k. The task is to decide whether T can be spanned by a subset of Esetminus T of size at most k. We prove that on graphic matroid perturbations, for every fixed r, Space Cover is fixed-parameter tractable parameterized by k. On the other hand, the problem becomes W[1]-hard when parameterized by r+k+|T| and it is NP-complete for rleq 2 and |T|leq 2. On cographic matroids, that are the duals of graphic matroids, Space Cover generalizes another fundamental and well-studied problem, namely Multiway Cut. We show that on the duals of perturbed graphic matroids the Space Cover problem is fixed-parameter tractable parameterized by r+k.
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 desired accuracy. This result subsequently leads to determine the universality of the approximation depending on the boundedness of the exponent function. Furthermore, whenever the exponent is unbounded, we obtain some characterization results for the subspace of functions that can be approximated.