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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 621630 of 1277 papers

TitleStatusHype
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer0
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling ProblemCode1
Smoothed Online Combinatorial Optimization Using Imperfect Predictions0
New Core-Guided and Hitting Set Algorithms for Multi-Objective Combinatorial Optimization0
Optimizing Tensor Network Contraction Using Reinforcement Learning0
Optimal Intermittent Particle FilterCode0
Application of QUBO solver using black-box optimization to structural design for resonance avoidance0
Energy-Sensitive Trajectory Design and Restoration Areas Allocation for UAV-Enabled Grassland Restoration0
Learning to Solve Travelling Salesman Problem with Hardness-adaptive CurriculumCode1
Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks0
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