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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

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Showing 521–530 of 1277 papers

TitleStatusHype
Enhancing Column Generation by Reinforcement Learning-Based Hyper-Heuristic for Vehicle Routing and Scheduling Problems—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention—0
An Improved ACS Algorithm for the Solutions of Larger TSP Problems—0
End-to-end Planning of Fixed Millimeter-Wave Networks—0
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location—0
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—0
Budgeted Influence Maximization for Multiple Products—0
An Expandable Machine Learning-Optimization Framework to Sequential Decision-Making—0
A Dynamic Programming Algorithm for Tree Trimming-based Text Summarization—0
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