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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 551560 of 1277 papers

TitleStatusHype
Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems0
Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint0
Robust Bayesian Inference for Moving Horizon Estimation0
Budget-Aware Sequential Brick Assembly with Efficient Constraint SatisfactionCode0
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-CutCode1
Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphs0
How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem0
Automatic and effective discovery of quantum kernelsCode0
Learning Obstacle-Avoiding Lattice Paths using Swarm Heuristics: Exploring the Bijection to Ordered Trees0
Structured Q-learning For Antibody Design0
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