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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 901910 of 1277 papers

TitleStatusHype
A Comparison of Greedy and Optimal Assessment of Natural Language Student Input Using Word-to-Word Similarity Metrics0
A Compositional Algorithm for the Conflict-Free Electric Vehicle Routing Problem0
A conditional gradient homotopy method with applications to Semidefinite Programming0
Active Instance Sampling via Matrix Partition0
Actively Learning Combinatorial Optimization Using a Membership Oracle0
Active Screening for Recurrent Diseases: A Reinforcement Learning Approach0
Adam assisted Fully informed Particle Swarm Optimzation ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA)0
Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training0
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry0
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