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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 161170 of 1277 papers

TitleStatusHype
Attention, Learn to Solve Routing Problems!Code1
HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMsCode1
Implicit MLE: Backpropagating Through Discrete Exponential Family DistributionsCode1
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-CutCode1
Instance-wise algorithm configuration with graph neural networksCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
L0Learn: A Scalable Package for Sparse Learning using L0 RegularizationCode1
Large Language Models as Evolutionary OptimizersCode1
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop SchedulingCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
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