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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 561570 of 1277 papers

TitleStatusHype
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications0
Cooperative coevolutionary hybrid NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective optimization0
Cool-Fusion: Fuse Large Language Models without Training0
How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods0
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems0
A Knowledge Representation Approach to Automated Mathematical Modelling0
Improvement/Extension of Modular Systems as Combinatorial Reengineering (Survey)0
How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem0
High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware0
Convergence and Running Time of Time-dependent Ant Colony Algorithms0
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