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A short review on novel approaches for maximum clique problem: from classical algorithms to graph neural networks and quantum algorithms
This manuscript provides a comprehensive review of the Maximum Clique Problem, a
computational problem that involves finding subsets of vertices in a graph that are all …
computational problem that involves finding subsets of vertices in a graph that are all …
Fuzzy Self-tuning Bees Algorithm for designing optimal product lines
Abstract The Product Line Design (PLD) problem is an NP-hard combinatorial optimization
problem in marketing that aims at determining an optimal product line through which a firm …
problem in marketing that aims at determining an optimal product line through which a firm …
Frequent pattern-based search: A case study on the quadratic assignment problem
We present frequent pattern-based search (FPBS) that combines data mining and
optimization. FPBS is a general-purpose method that unifies data mining and optimization …
optimization. FPBS is a general-purpose method that unifies data mining and optimization …
Lense: Learning to navigate subgraph embeddings for large-scale combinatorial optimisation
Combinatorial Optimisation problems arise in several application domains and are often
formulated in terms of graphs. Many of these problems are NP-hard, but exact solutions are …
formulated in terms of graphs. Many of these problems are NP-hard, but exact solutions are …
Metaheuristic-based hyperparameter optimization for multi-disease detection and diagnosis in machine learning
Metaheuristic algorithms with machine learning techniques have become popular because it
works so well for problems like regression, classification, rule mining, and clustering in …
works so well for problems like regression, classification, rule mining, and clustering in …
COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
H Tian, S Medya, W Ye - Proceedings of the AAAI Conference on …, 2024 - ojs.aaai.org
Combinatorial Optimization (CO) problems over graphs appear routinely in many
applications such as in optimizing traffic, viral marketing in social networks, and matching for …
applications such as in optimizing traffic, viral marketing in social networks, and matching for …
Geometric deep learning sub-network extraction for maximum clique enumeration
The paper presents an algorithm to approach the problem of Maximum Clique Enumeration,
a well known NP-hard problem that have several real world applications. The proposed …
a well known NP-hard problem that have several real world applications. The proposed …
[HTML][HTML] Using Machine Learning in Combinatorial Optimization: Extraction of Graph Features for Travelling Salesman Problem
P Stodola, R Ščurek - Knowledge-Based Systems, 2025 - Elsevier
Abstract Machine learning has emerged as a paradigmatic approach for addressing
complex problems across various scientific disciplines, including combinatorial optimization …
complex problems across various scientific disciplines, including combinatorial optimization …
Open problems in (hyper) graph decomposition
Large networks are useful in a wide range of applications. Sometimes problem instances
are composed of billions of entities. Decomposing and analyzing these structures helps us …
are composed of billions of entities. Decomposing and analyzing these structures helps us …
Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization
Modern instances of combinatorial optimization problems often exhibit billion-scale ground
sets, which have many uninformative or redundant elements. In this work, we develop light …
sets, which have many uninformative or redundant elements. In this work, we develop light …