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We prove the first polynomial separation between randomized and deterministic time-space tradeoffs of multi-output functions. In particular, we present a total function that on the input of $n$ elements in $[n]$, outputs $O(n)$ elements,…

Computational Complexity · Computer Science 2023-06-29 Huacheng Yu , Wei Zhan

Problems defined on binary decision spaces have been intensively studied in the theory of multi-objective evolutionary algorithms (MOEAs). In contrast, no mathematical runtime analyses exist so far for MOEAs dealing with decision variables…

Neural and Evolutionary Computing · Computer Science 2026-05-15 Mingfeng Li , Zheng Cheng , Weijie Zheng , Benjamin Doerr

This paper explores the distribution of indistinguishable balls into distinct urns with varying capacity constraints, a foundational issue in combinatorial mathematics with applications across various disciplines. We present a comprehensive…

Probability · Mathematics 2025-02-07 Jingwei Li , Thomas G. Robertazzi

Let $P$ be a set of $n$ points in the plane, where each element of $P$ is assigned a weight $\omega(p)$, positive or negative. In this paper, we present an algorithm that runs in $O(n^4\log n)$ time and $O(n)$ space to find two possibly…

Computational Geometry · Computer Science 2026-05-22 José Fernández Goycoolea , Luis H. Herrera , Pablo Pérez Lantero , Carlos Seara

Many algorithms in machine learning and computational geometry require, as input, the intrinsic dimension of the manifold that supports the probability distribution of the data. This parameter is rarely known and therefore has to be…

Statistics Theory · Mathematics 2020-01-01 Jisu Kim , Alessandro Rinaldo , Larry Wasserman

One of the key issues regarding classification problems in Trustworthy Artificial Intelligence is ensuring Fairness in the prediction of different classes when protected (sensitive) features are present. Data quality is critical in these…

Machine Learning · Computer Science 2025-03-13 José Daniel Pascual-Triana , Alberto Fernández , Paulo Novais , Francisco Herrera

We study the multinomial logit (MNL) bandit problem, where at each time step, the seller offers an assortment of size at most $K$ from a pool of $N$ items, and the buyer purchases an item from the assortment according to a MNL choice model.…

Machine Learning · Computer Science 2020-11-20 Jiaqi Yang

This paper studies equity basket options -- i.e., multi-dimensional derivatives whose payoffs depend on the value of a weighted sum of the underlying stocks -- and develops a new and innovative approach to ensure consistency between options…

Computational Finance · Quantitative Finance 2022-06-22 Lech A. Grzelak , Juliusz Jablecki , Dariusz Gatarek

We consider the problem of deterministic load balancing of tokens in the discrete model. A set of $n$ processors is connected into a $d$-regular undirected network. In every time step, each processor exchanges some of its tokens with each…

Data Structures and Algorithms · Computer Science 2015-02-24 Petra Berenbrink , Ralf Klasing , Adrian Kosowski , Frederik Mallmann-Trenn , Przemyslaw Uznanski

Rebalancing schemes for dynamic binary search trees are numerous in the literature, where the goal is to maintain trees of low height, either in the worst-case or expected sense. In this paper we study randomized rebalancing schemes for…

Data Structures and Algorithms · Computer Science 2024-04-15 Gerth Stølting Brodal

Mean-payoff games play a central role in quantitative synthesis and verification. In a single-dimensional game a weight is assigned to every transition and the objective of the protagonist is to assure a non-negative limit-average weight.…

Logic in Computer Science · Computer Science 2014-10-22 Yaron Velner

We study the Maximum Budgeted Allocation problem, i.e., the problem of selling a set of $m$ indivisible goods to $n$ players, each with a separate budget, such that we maximize the collected revenue. Since the natural assignment LP is known…

Data Structures and Algorithms · Computer Science 2014-03-31 Christos Kalaitzis , Aleksander Mcadry , Alantha Newman , Lukáš Poláček , Ola Svensson

In the classical Polya urn problem, one begins with $d$ bins, each containing one ball. Additional balls arrive one at a time, and the probability that an arriving ball is placed in a given bin is proportional to $m^\gamma$, where $m$ is…

Probability · Mathematics 2014-07-01 Jeremy Chen

Motivated by bursty bandwidth allocation and by the allocation of virtual machines to servers in the cloud, we consider the online problem of packing items with random sizes into unit-capacity bins. Items arrive sequentially, but upon…

Optimization and Control · Mathematics 2021-02-08 Sebastian Perez-Salazar , Mohit Singh , Alejandro Toriello

Multidimensional packing problems generalize the classical packing problems such as Bin Packing, Multiprocessor Scheduling by allowing the jobs to be $d$-dimensional vectors. While the approximability of the scalar problems is well…

Data Structures and Algorithms · Computer Science 2021-06-04 Sai Sandeep

We consider the problem of subset selection where one is given multiple rankings of items and the goal is to select the highest ``quality'' subset. Score functions from the multiwinner voting literature have been used to aggregate rankings…

Computers and Society · Computer Science 2023-06-19 Niclas Boehmer , L. Elisa Celis , Lingxiao Huang , Anay Mehrotra , Nisheeth K. Vishnoi

Finding nonoverlapping balls with given centers in any metric space, maximizing the sum of radii of the balls, can be expressed as a linear program. Its dual linear program expresses the problem of finding a minimum-weight set of cycles…

Computational Geometry · Computer Science 2017-10-09 David Eppstein

Consider a model of $N$ independent, increasing $\mathbb{N}_0$-valued processes, with random, independent waiting times between jumps. It is known that there is either an emergent `leader', in which a single process possesses the maximal…

Probability · Mathematics 2025-10-09 Johannes Bäumler , Tejas Iyer

The 2D Online Bin Packing is a fundamental problem in Computer Science and the determination of its asymptotic competitive ratio has attracted great research attention. In a long series of papers, the lower bound of this ratio has been…

Data Structures and Algorithms · Computer Science 2009-06-03 Xin Han , Francis Y. L. Chin , Hing-Fung Ting , Guochuan Zhang

A runtime analysis of the Univariate Marginal Distribution Algorithm (UMDA) is presented on the OneMax function for wide ranges of its parameters $\mu$ and $\lambda$. If $\mu\ge c\log n$ for some constant $c>0$ and…

Neural and Evolutionary Computing · Computer Science 2018-06-08 Carsten Witt
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