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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 576–600 of 1277 papers

TitleStatusHype
Highly parallel algorithm for the Ising ground state searching problem—0
Artificial Catalytic Reactions in 2D for Combinatorial Optimization—0
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows—0
Implicitly Intersecting Weighted Automata using Dual Decomposition—0
High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET—0
A Knowledge-Based Approach to Word Sense Disambiguation by distributional selection and semantic features—0
Improved Approximation Algorithms for Low-Rank Problems Using Semidefinite Optimization—0
Higher-Order Quantum-Inspired Genetic Algorithms—0
Improvement/Extension of Modular Systems as Combinatorial Reengineering (Survey)—0
Improving Existing Optimization Algorithms with LLMs—0
Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach—0
Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability—0
High-Dimensional Prediction for Sequential Decision Making—0
Inference in Graphical Models via Semidefinite Programming Hierarchies—0
Inference of hidden structures in complex physical systems by multi-scale clustering—0
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity—0
INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis—0
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis—0
Initialization Method for Factorization Machine Based on Low-Rank Approximation for Constructing a Corrected Approximate Ising Model—0
Hierarchical Clustering: Objective Functions and Algorithms—0
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization—0
Convergence Acceleration of Markov Chain Monte Carlo-based Gradient Descent by Deep Unfolding—0
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications—0
DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problem—0
Joint User Pairing and Association for Multicell NOMA: A Pointer Network-based Approach—0
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