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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 171180 of 1277 papers

TitleStatusHype
Exploring the Loss Landscape in Neural Architecture SearchCode1
A Learning-based Iterative Method for Solving Vehicle Routing ProblemsCode1
Rethinking Differentiable Search for Mixed-Precision Neural NetworksCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
A Bayesian algorithm for retrosynthesisCode1
Incremental Sampling Without Replacement for Sequence ModelsCode1
Learn to Design the Heuristics for Vehicle Routing ProblemCode1
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Targeted sampling of enlarged neighborhood via Monte Carlo tree search for TSPCode1
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