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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 151175 of 1277 papers

TitleStatusHype
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural NetworksCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph MatchingCode1
Learning What to Defer for Maximum Independent SetsCode1
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setCode1
Learn to Design the Heuristics for Vehicle Routing ProblemCode1
A Bayesian algorithm for retrosynthesisCode1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
Attention, Learn to Solve Routing Problems!Code1
Discovering Dynamic Causal Space for DAG Structure LearningCode1
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Hybrid Pointer Networks for Traveling Salesman Problems OptimizationCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
Denoising Autoencoders for fast Combinatorial Black Box OptimizationCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial OptimizationCode1
Instance-wise algorithm configuration with graph neural networksCode1
Learning Large Neighborhood Search for Vehicle Routing in Airport Ground HandlingCode1
Exploring the Loss Landscape in Neural Architecture SearchCode1
Moco: A Learnable Meta Optimizer for Combinatorial OptimizationCode1
Noisy intermediate-scale quantum algorithm for semidefinite programmingCode1
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