Full version of APPROX 2024 paper

Oct 13, 2025 by talynt.org 124 views

Latest 15 Papers - March 10, 2025

Optimizing Complex Problems with the Latest Research

In the field of computer science and mathematics, researchers are constantly working on solving complex problems that have real-world applications. Two such problems are the knapsack problem and the minimum cut problem. The knapsack problem involves finding the optimal way to pack items of different weights and values into a knapsack of limited capacity, while the minimum cut problem involves finding the minimum cut in a graph that separates two sets of nodes. In this article, we will discuss the latest 15 papers on these two problems, highlighting their key findings and contributions.

Knapsack

The knapsack problem is a classic problem in combinatorial optimization that has numerous applications in logistics, finance, and other fields. The problem involves finding the optimal way to pack items of different weights and values into a knapsack of limited capacity. The goal is to maximize the total value of the items packed while not exceeding the capacity of the knapsack.

Title	Date	Comment
Constrained Bandwidth Observation Sharing for Multi-Robot Navigation in Dynamic Environments via Intelligent Knapsack	2025-03-03
Sum-Of-Squares To Approximate Knapsack	2025-02-18
Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation	2025-02-18
Generative-enhanced optimization for knapsack problems: an industry-relevant study	2025-02-07
Bandits with Anytime Knapsacks	2025-01-30
A Nearly Quadratic-Time FPTAS for Knapsack	2025-01-07
Hybrid Firefly-Genetic Algorithm for Single and Multi-dimensional 0-1 Knapsack Problems	2024-12-31
Approximation Schemes for Geometric Knapsack for Packing Spheres and Fat Objects	2024-12-23	A pre... A preliminary version of the work appeared in the proceedings of the 51st EATCS International Colloquium on Automata, Languages, and Programming (ICALP) 2024
The complexity of knapsack problems in wreath products	2024-11-30
Approximation Algorithms for Correlated Knapsack Orienteering	2024-11-29	Full ... Full version of APPROX 2024 paper
Shadoks Approach to Knapsack Polygonal Packing	2024-11-28
Online Unbounded Knapsack	2024-10-31
Approximately Counting Knapsack Solutions in Subquadratic Time	2024-10-29	To ap... To appear at SODA 2025
Maximizing a Submodular Function with Bounded Curvature under an Unknown Knapsack Constraint	2024-10-24
Packing a Knapsack with Items Owned by Strategic Agents	2024-10-08

These papers present various approaches to solving the knapsack problem, including approximation algorithms, exact algorithms, and hybrid methods. Some of the papers focus on specific variants of the knapsack problem, such as the geometric knapsack problem or the correlated knapsack problem.

Minimum Cut

The minimum cut problem is a classic problem in graph theory that has numerous applications in network design, logistics, and other fields. The problem involves finding the minimum cut in a graph that separates two sets of nodes. The goal is to minimize the total weight of the edges in the cut.

Title	Date	Comment
An O(log n)-Approximation Algorithm for (p,q)-Flexible Graph Connectivity via Independent Rounding	2025-01-22	11 pages
Fully Dynamic Approximate Minimum Cut in Subpolynomial Time per Operation	2025-01-06	To ap... To appear at SODA2025
Space Complexity of Minimum Cut Problems in Single-Pass Streams	2024-12-06	25+3 ... 25+3 pages, 2 figures. Accepted to ITCS 2025. v2: minor updates to author information
Tree-Packing Revisited: Faster Fully Dynamic Min-Cut and Arboricity	2024-12-04	To be... To be presented at SODA '25
Approximating the Held-Karp Bound for Metric TSP in Nearly Linear Work and Polylogarithmic Depth	2024-11-22
Linear-Time Algorithms for k-Edge-Connected Components, k-Lean Tree Decompositions, and More	2024-11-04	107 pages
A Simpler Approach for Monotone Parametric Minimum Cut: Finding the Breakpoints in Order	2024-10-21
Graph Cuts with Arbitrary Size Constraints Through Optimal Transport	2024-10-04	Publi... Published in Transactions on Machine Learning Research
Optimal Sensitivity Oracle for Steiner Mincut	2024-09-26
Mimicking Networks for Constrained Multicuts in Hypergraphs	2024-09-20	Accep... Accepted to appear in proceedings of ISAAC 2024
Finding Diverse Minimum s-t Cuts	2024-09-18	An ea... An earlier version of this work appeared at the 34th International Symposium on Algorithms and Computation (ISAAC 2023). Corrected typos in Section 3 and revised arguments in Section 4. Results unchanged. Added new complexity results in Section 5. Readded missing acknowledgments section
The Parameterized Complexity Landscape of Two-Sets Cut-Uncut	2024-08-24
Distributional limits of graph cuts on discretized grids	2024-07-21	59 pages, 11 figures
Interdiction of minimum spanning trees and other matroid bases	2024-07-20	29 pages, 2 figures
Tight Lower Bounds for Directed Cut Sparsification and Distributed Min-Cut	2024-06-19

These papers
Q&A: Latest 15 Papers on Knapsack and Minimum Cut Problems

In our previous article, we discussed the latest 15 papers on knapsack and minimum cut problems. These papers present various approaches to solving these complex problems, including approximation algorithms, exact algorithms, and hybrid methods. In this article, we will answer some of the most frequently asked questions about these papers and provide additional insights into the research.

Q: What is the knapsack problem, and why is it important?

A: The knapsack problem is a classic problem in combinatorial optimization that involves finding the optimal way to pack items of different weights and values into a knapsack of limited capacity. The problem is important because it has numerous applications in logistics, finance, and other fields, such as:

Logistics: The knapsack problem is used to optimize the loading of cargo onto trucks or planes, ensuring that the maximum value of goods is transported while minimizing the weight.
Finance: The knapsack problem is used to optimize investment portfolios, ensuring that the maximum return on investment is achieved while minimizing risk.
Supply Chain Management: The knapsack problem is used to optimize the allocation of resources in supply chains, ensuring that the maximum value of goods is produced while minimizing waste.

Q: What is the minimum cut problem, and why is it important?

A: The minimum cut problem is a classic problem in graph theory that involves finding the minimum cut in a graph that separates two sets of nodes. The problem is important because it has numerous applications in network design, logistics, and other fields, such as:

Network Design: The minimum cut problem is used to design networks that minimize the cost of communication or transportation between nodes.
Logistics: The minimum cut problem is used to optimize the routing of goods or services, ensuring that the minimum cost is incurred while meeting demand.
Supply Chain Management: The minimum cut problem is used to optimize the allocation of resources in supply chains, ensuring that the minimum cost is incurred while meeting demand.

Q: What are some of the key findings of the latest 15 papers on knapsack and minimum cut problems?

A: Some of the key findings of the latest 15 papers on knapsack and minimum cut problems include:

Approximation algorithms: Several papers present approximation algorithms for solving the knapsack problem, including the geometric knapsack problem and the correlated knapsack problem.
Exact algorithms: Several papers present exact algorithms for solving the minimum cut problem, including the O(log n)-approximation algorithm for (p,q)-flexible graph connectivity.
Hybrid methods: Several papers present hybrid methods for solving the knapsack problem, including the hybrid firefly-genetic algorithm for single and multi-dimensional 0-1 knapsack problems.

Q: What are some of the challenges and limitations of the latest 15 papers on knapsack and minimum cut problems?

A: Some of the challenges and limitations of the latest 15 papers on knapsack and minimum cut problems include:

Computational complexity: Several papers present algorithms that have high computational complexity, making them impractical for large-scale problems.
Scalability: Several papers present algorithms that are not scalable, making them impractical for large-scale problems.
Robustness: Several papers present algorithms that are not robust, making them sensitive to changes in the input data.

Q: What are some of the future directions for research on knapsack and minimum cut problems?

A: Some of the future directions for research on knapsack and minimum cut problems include:

Developing more efficient algorithms: Researchers are working on developing more efficient algorithms for solving the knapsack problem and the minimum cut problem.
Improving the scalability of algorithms: Researchers are working on improving the scalability of algorithms for solving the knapsack problem and the minimum cut problem.
Developing more robust algorithms: Researchers are working on developing more robust algorithms for solving the knapsack problem and the minimum cut problem.

Q: How can readers access the latest 15 papers on knapsack and minimum cut problems?

A: Readers can access the latest 15 papers on knapsack and minimum cut problems by visiting the following websites:

ArXiv: The ArXiv website provides access to the latest papers on knapsack and minimum cut problems.
ResearchGate: The ResearchGate website provides access to the latest papers on knapsack and minimum cut problems.
Academic databases: The academic databases, such as Google Scholar and Microsoft Academic, provide access to the latest papers on knapsack and minimum cut problems.

We hope that this Q&A article has provided additional insights into the research on knapsack and minimum cut problems. If you have any further questions, please do not hesitate to contact us.