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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 7180 of 1277 papers

TitleStatusHype
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial OptimizationCode1
Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems0
Unsupervised Learning for Quadratic Assignment0
Neural Combinatorial Optimization for Real-World RoutingCode1
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic ProgrammingCode0
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware0
FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data ClassificationCode1
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation0
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms0
Towards Constraint-Based Adaptive Hypergraph Learning for Solving Vehicle Routing: An End-to-End Solution0
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