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In the classical Subset Sum problem we are given a set $X$ and a target $t$, and the task is to decide whether there exists a subset of $X$ which sums to $t$. A recent line of research has resulted in $\tilde{O}(t)$-time algorithms, which…

Data Structures and Algorithms · Computer Science 2023-04-25 Karl Bringmann , Vasileios Nakos

For many constraint satisfaction problems, the algorithm which chooses a random assignment achieves the best possible approximation ratio. For instance, a simple random assignment for {\sc Max-E3-Sat} allows 7/8-approximation and for every…

Data Structures and Algorithms · Computer Science 2011-10-17 Eun Jung Kim , Ryan Williams

We give the first O(m polylog(n)) time algorithms for approximating maximum flows in undirected graphs and constructing polylog(n) -quality cut-approximating hierarchical tree decompositions. Our algorithm invokes existing algorithms for…

Data Structures and Algorithms · Computer Science 2015-11-18 Richard Peng

We give new rounding schemes for SDP relaxations for the problems of maximizing cubic polynomials over the unit sphere and the $n$-dimensional hypercube. In both cases, the resulting algorithms yield a $O(\sqrt{n/k})$ multiplicative…

Data Structures and Algorithms · Computer Science 2023-10-03 Jun-Ting Hsieh , Pravesh K. Kothari , Lucas Pesenti , Luca Trevisan

We take an algorithmic approach to studying the solution space geometry of relatively sparse random and bounded degree $k$-CNFs for large $k$. In the course of doing so, we establish that with high probability, a random $k$-CNF $\Phi$ with…

Data Structures and Algorithms · Computer Science 2022-07-18 Zongchen Chen , Nitya Mani , Ankur Moitra

The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions, thus has been widely adopted in numerous statistical…

Machine Learning · Statistics 2022-05-17 Yuxin Dong , Tieliang Gong , Shujian Yu , Chen Li

In this paper we propose a structural parameter of CNF formulas and use it to identify instances of weighted MaxSAT and #SAT that can be solved in polynomial time. Given a CNF formula we say that a set of clauses is precisely satisfiable if…

Data Structures and Algorithms · Computer Science 2014-02-27 Sigve Hortemo Sæther , Jan Arne Telle , Martin Vatshelle

We study the general scheduling problem (GSP) which generalizes and unifies several well-studied preemptive single-machine scheduling problems, such as weighted flow time, weighted sum of completion time, and minimizing the total weight of…

Data Structures and Algorithms · Computer Science 2025-11-21 Alexander Armbruster , Lars Rohwedder , Andreas Wiese

We consider the Max-$3$-Section problem, where we are given an undirected graph $ G=(V,E)$ equipped with non-negative edge weights $w :E\rightarrow \mathbb{R}_+$ and the goal is to find a partition of $V$ into three equisized parts while…

Data Structures and Algorithms · Computer Science 2023-08-08 Dor Katzelnick , Aditya Pillai , Roy Schwartz , Mohit Singh

In this paper, we study a class of approximation problems, appearing in data approximation and signal processing. The approximations are constructed as combinations of polynomial splines (piecewise polynomials), whose parameters are subject…

Optimization and Control · Mathematics 2015-03-05 Zahra Roshan Zamir , Nadezda Sukhorukova

For any $\varepsilon > 0$, we prove that $k$-Dimensional Matching is hard to approximate within a factor of $k/(12 + \varepsilon)$ for large $k$ unless $\textsf{NP} \subseteq \textsf{BPP}$. Listed in Karp's 21 $\textsf{NP}$-complete…

Computational Complexity · Computer Science 2024-09-27 Euiwoong Lee , Ola Svensson , Theophile Thiery

The MaxClique problem, finding the largest complete subgraph in an Erd{\"o}s-R{\'e}nyi $G(N,p)$ random graph in the large $N$ limit, is a well-known example of a simple problem for which finding any approximate solution within a factor of…

Data Structures and Algorithms · Computer Science 2023-05-26 Raffaele Marino , Scott Kirkpatrick

It has been observed in many places that constant-factor approximable problems often admit polynomial or even linear problem kernels for their decision versions, e.g., Vertex Cover, Feedback Vertex Set, and Triangle Packing. While there…

Computational Complexity · Computer Science 2015-03-13 Stefan Kratsch

Reinforcement learning with function approximation has recently achieved tremendous results in applications with large state spaces. This empirical success has motivated a growing body of theoretical work proposing necessary and sufficient…

Machine Learning · Computer Science 2022-07-05 Daniel Kane , Sihan Liu , Shachar Lovett , Gaurav Mahajan

Maximizing a non-negative, monontone, submodular function $f$ over $n$ elements under a cardinality constraint $k$ (SMCC) is a well-studied NP-hard problem. It has important applications in, e.g., machine learning and influence…

Data Structures and Algorithms · Computer Science 2024-02-05 Philip Cervenjak , Junhao Gan , Anthony Wirth

We provide a new approach for establishing hardness of approximation results, based on the theory recently introduced by the author. It allows one to directly show that approximating a problem beyond a certain threshold requires…

Computational Complexity · Computer Science 2024-02-23 Ali Çivril

The Knapsack problem is one of the most fundamental NP-complete problems at the intersection of computer science, optimization, and operations research. A recent line of research worked towards understanding the complexity of…

Data Structures and Algorithms · Computer Science 2024-02-27 Karl Bringmann

In the unsplittable flow problem on a path, we are given a capacitated path $P$ and $n$ tasks, each task having a demand, a profit, and start and end vertices. The goal is to compute a maximum profit set of tasks, such that for each edge…

Data Structures and Algorithms · Computer Science 2015-03-19 Paul Bonsma , Jens Schulz , Andreas Wiese

We continue the investigation of polynomial-time sparsification for NP-complete Boolean Constraint Satisfaction Problems (CSPs). The goal in sparsification is to reduce the number of constraints in a problem instance without changing the…

Computational Complexity · Computer Science 2018-09-18 Hubie Chen , Bart M. P. Jansen , Astrid Pieterse

The independence number of a tree decomposition is the size of a largest independent set contained in a single bag. The tree-independence number of a graph $G$ is the minimum independence number of a tree decomposition of $G$. As shown…

Data Structures and Algorithms · Computer Science 2026-01-23 Daniel Lokshtanov , Michał Pilipczuk , Paweł Rzążewski
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