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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

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Showing 126–150 of 1277 papers

TitleStatusHype
Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Geometric Deep Reinforcement Learning for Dynamic DAG SchedulingCode1
Attention, Learn to Solve Routing Problems!Code1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
Feature Importance Ranking for Deep LearningCode1
Automatic Truss Design with Reinforcement LearningCode1
FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data ClassificationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Belief Propagation Neural NetworksCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Hybrid Pointer Networks for Traveling Salesman Problems OptimizationCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approachCode1
A Learning-based Iterative Method for Solving Vehicle Routing ProblemsCode1
ASP: Learn a Universal Neural Solver!Code1
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial OptimizationCode1
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot TeamsCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
GOAL: A Generalist Combinatorial Optimization Agent LearningCode1
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