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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 941950 of 1277 papers

TitleStatusHype
Ising-based Consensus Clustering on Specialized Hardware0
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective0
The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery0
Sparse Optimization for Green Edge AI Inference0
Learning Interpretable Error Functions for Combinatorial Optimization Problem ModelingCode0
Incremental Sampling Without Replacement for Sequence ModelsCode1
Learn to Design the Heuristics for Vehicle Routing ProblemCode1
Constrained Multiagent Rollout and Multidimensional Assignment with the Auction Algorithm0
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives0
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
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