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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 231240 of 1277 papers

TitleStatusHype
Graph Neural Networks for Job Shop Scheduling Problems: A Survey0
Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization0
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity AnalysisCode0
A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks0
Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization0
Intertwining CP and NLP: The Generation of Unreasonably Constrained Sentences0
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning0
A Benchmark for Maximum Cut: Towards Standardization of the Evaluation of Learned Heuristics for Combinatorial OptimizationCode0
Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential HeuristicsCode0
Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment ProblemCode1
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