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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 891900 of 1277 papers

TitleStatusHype
Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleet0
Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization0
Leveraging Conflicting Constraints in Solving Vehicle Routing Problems0
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem0
LIAR: Leveraging Alignment (Best-of-N) to Jailbreak LLMs in Seconds0
Linear Inverse Problems with Norm and Sparsity Constraints0
Liner Shipping Network Design with Reinforcement Learning0
Link Prediction with Untrained Message Passing Layers0
LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning0
LMask: Learn to Solve Constrained Routing Problems with Lazy Masking0
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