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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

Showing 541550 of 1277 papers

TitleStatusHype
Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning0
Active Screening for Recurrent Diseases: A Reinforcement Learning Approach0
How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods0
Currency Arbitrage Optimization using Quantum Annealing, QAOA and Constraint Mapping0
A Spectral Method for Unsupervised Multi-Document Summarization0
Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach0
CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs0
High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware0
A Simulated Annealing-Based Multiobjective Optimization Algorithm for Minimum Weight Minimum Connected Dominating Set Problem0
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