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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 961970 of 1277 papers

TitleStatusHype
Near-Optimal LOS and Orientation Aware Intelligent Reflecting Surface Placement0
Energy-Sensitive Trajectory Design and Restoration Areas Allocation for UAV-Enabled Grassland Restoration0
Learning to Select and Rank from Choice-Based Feedback: A Simple Nested Approach0
Network Interdiction Goes Neural0
Neural Bee Colony Optimization: A Case Study in Public Transit Network Design0
Neural Combinatorial Optimization: a New Player in the Field0
Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure0
Neural Combinatorial Optimization via Preference Optimization0
Neural Combinatorial Optimization with Reinforcement Learning : Solving theVehicle Routing Problem with Time Windows0
Neural Extensions: Training Neural Networks with Set Functions0
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