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In this paper a tight bound on the worst-case number of comparisons for Floyd's well known heap construction algorithm, is derived. It is shown that at most 2n-2{\mu}(n)-{\sigma}(n) comparisons are executed in the worst case, where {\mu}(n)…

Data Structures and Algorithms · Computer Science 2015-03-17 Ioannis Paparrizos

We make three different types of contributions to cost-sharing: First, we identify several new classes of combinatorial cost functions that admit incentive-compatible mechanisms achieving both a constant-factor approximation of…

Computer Science and Game Theory · Computer Science 2007-05-23 Tim Roughgarden , Mukund Sundararajan

Explorable heap selection is the problem of selecting the $n$th smallest value in a binary heap. The key values can only be accessed by traversing through the underlying infinite binary tree, and the complexity of the algorithm is measured…

Data Structures and Algorithms · Computer Science 2024-09-12 Sander Borst , Daniel Dadush , Sophie Huiberts , Danish Kashaev

This paper focuses on the further development of the Lie bracket approximation approach for optimization and control via extremum seeking systems. Classical results in this area provide algorithms with exponential convergence rates for…

Optimization and Control · Mathematics 2026-05-25 Victoria Grushkovskaya , Sameh A. Eisa

Many algorithms for maximizing a monotone submodular function subject to a knapsack constraint rely on the natural greedy heuristic. We present a novel refined analysis of this greedy heuristic which enables us to: $(1)$ reduce the…

Data Structures and Algorithms · Computer Science 2021-03-16 Ariel Kulik , Roy Schwartz , Hadas Shachnai

Bin Packing with $k$ bins is a fundamental optimisation problem in which we are given a set of $n$ integers and a capacity $T$ and the goal is to partition the set into $k$ subsets, each of total sum at most $T$. Bin Packing is NP-hard…

Data Structures and Algorithms · Computer Science 2026-03-16 Karl Bringmann , Anita Dürr , Karol Węgrzycki

We consider derivatives written on multiple underlyings in a one-period financial market, and we are interested in the computation of model-free upper and lower bounds for their arbitrage-free prices. We work in a completely realistic…

Optimization and Control · Mathematics 2022-01-13 Ariel Neufeld , Antonis Papapantoleon , Qikun Xiang

A tree-packing is a collection of spanning trees of a graph. It has been a useful tool for computing the minimum cut in static, dynamic, and distributed settings. In particular, [Thorup, Comb. 2007] used them to obtain his dynamic min-cut…

Data Structures and Algorithms · Computer Science 2024-12-05 Tijn de Vos , Aleksander B. G. Christiansen

Join ordering is the NP-hard problem of selecting the most efficient order in which to evaluate joins (conjunctive, binary operators) in a database query. Because query execution performance critically depends on this choice, join ordering…

Databases · Computer Science 2026-05-18 Tim Schwabe , Maribel Acosta

We numerically investigate the performance of the short path optimization algorithm on a toy problem, with the potential chosen to depend only on the total Hamming weight to allow simulation of larger systems. We consider classes of…

Quantum Physics · Physics 2019-05-22 M. B. Hastings

The minimum and maximum cuts of an undirected edge-weighted graph are classic problems in graph theory. While the Min-Cut Problem can be solved in P, the Max-Cut Problem is NP-Complete. Exact and heuristic methods have been developed for…

Combinatorics · Mathematics 2023-08-15 Justo Puerto , José L. Sainz-Pardo

Smoothed analysis of multiobjective 0-1 linear optimization has drawn considerable attention recently. The number of Pareto-optimal solutions (i.e., solutions with the property that no other solution is at least as good in all the…

Data Structures and Algorithms · Computer Science 2011-07-21 Navin Goyal , Luis Rademacher

We formulate the entropy of a quantized artificial neural network as a differentiable function that can be plugged as a regularization term into the cost function minimized by gradient descent. Our formulation scales efficiently beyond the…

Machine Learning · Computer Science 2021-07-13 Enzo Tartaglione , Stéphane Lathuilière , Attilio Fiandrotti , Marco Cagnazzo , Marco Grangetto

We give polynomial time logarithmic approximation guarantees for the budget minimization, as well as for the profit maximization versions of minimum spanning tree interdiction. In this problem, the goal is to remove some edges of an…

Data Structures and Algorithms · Computer Science 2025-07-28 Rafail Ostrovsky , Yuval Rabani , Yoav Siman Tov

We present a novel algorithm for the minimum-depth elimination tree problem, which is equivalent to the optimal treedepth decomposition problem. Our algorithm makes use of two cheaply-computed lower bound functions to prune the search tree,…

Discrete Mathematics · Computer Science 2020-06-18 James Trimble

We prove lower bounds for higher-order methods in smooth non-convex finite-sum optimization. Our contribution is threefold: We first show that a deterministic algorithm cannot profit from the finite-sum structure of the objective, and that…

Optimization and Control · Mathematics 2021-07-05 Nicolas Emmenegger , Rasmus Kyng , Ahad N. Zehmakan

In the last decade, there has been a substantial amount of research in finding routing algorithms designed specifically to run on real-world graphs. In 2010, Abraham et al. showed upper bounds on the query time in terms of a graph's highway…

Data Structures and Algorithms · Computer Science 2015-09-08 Colin White

Conditional lower bounds based on $P\neq NP$, the Exponential-Time Hypothesis (ETH), or similar complexity assumptions can provide very useful information about what type of algorithms are likely to be possible. Ideally, such lower bounds…

Computational Complexity · Computer Science 2022-03-16 Dániel Marx

We give new approximation algorithms for the submodular joint replenishment problem and the inventory routing problem, using an iterative rounding approach. In both problems, we are given a set of $N$ items and a discrete time horizon of…

Data Structures and Algorithms · Computer Science 2019-12-03 Thomas Bosman , Neil Olver

This paper studies a non-stationary kernelized bandit (KB) problem, also called time-varying Bayesian optimization, where one seeks to minimize the regret under an unknown reward function that varies over time. In particular, we focus on a…

Machine Learning · Computer Science 2024-10-22 Shogo Iwazaki , Shion Takeno