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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 271280 of 1277 papers

TitleStatusHype
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization ProblemsCode0
A2Perf: Real-World Autonomous Agents Benchmark0
Lattice Protein Folding with Variational Annealing0
Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization0
optimizn: a Python Library for Developing Customized Optimization Algorithms0
Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc0
Destroy and Repair Using Hyper Graphs for RoutingCode0
Synthesizing Composite Hierarchical Structure from Symbolic Music CorporaCode0
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization FormulationsCode0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks0
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