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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 841–850 of 1277 papers

TitleStatusHype
Leader Reward for POMO-Based Neural Combinatorial Optimization—0
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems—0
Learning-based Compressive Subsampling—0
Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization—0
Learning-based Memetic Algorithm for Hard-label Textual Attack—0
Learning-Based TSP-Solvers Tend to Be Overly Greedy—0
Learning Branching Heuristics from Graph Neural Networks—0
Learning chordal extensions—0
Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking—0
Learning Combinatorial Solver for Graph Matching—0
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