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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 861870 of 1277 papers

TitleStatusHype
Learning Obstacle-Avoiding Lattice Paths using Swarm Heuristics: Exploring the Bijection to Ordered Trees0
Interpretable Decision Trees Through MaxSAT0
Learning Pseudo-Backdoors for Mixed Integer Programs0
Learning (Re-)Starting Solutions for Vehicle Routing Problems0
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs0
Learning Self-Game-Play Agents for Combinatorial Optimization Problems0
Learning the Quality of Machine Permutations in Job Shop Scheduling0
Learning to Branch in Combinatorial Optimization with Graph Pointer Networks0
Learning To Dive In Branch And Bound0
Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks0
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