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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 10811090 of 1277 papers

TitleStatusHype
Neural Improvement Heuristics for Graph Combinatorial Optimization ProblemsCode0
Neural Knapsack: A Neural Network Based Solver for the Knapsack ProblemCode0
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
Latent Guided Sampling for Combinatorial OptimizationCode0
A GREAT Architecture for Edge-Based Graph Problems Like TSPCode0
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural NetworksCode0
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language ModelsCode0
COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial ProblemsCode0
Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence ModelCode0
Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential HeuristicsCode0
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