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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 191200 of 1277 papers

TitleStatusHype
Rethinking Differentiable Search for Mixed-Precision Neural NetworksCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
Lagrange Oscillatory Neural Networks for Constraint Satisfaction and OptimizationCode0
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment ProblemCode0
Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set ProgrammingCode0
An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut ProblemCode0
Ants can orienteer a thief in their robberyCode0
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, ApplicationsCode0
Latent Guided Sampling for Combinatorial OptimizationCode0
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