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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 101110 of 1277 papers

TitleStatusHype
Geometric Deep Reinforcement Learning for Dynamic DAG SchedulingCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMsCode1
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* NeighborhoodCode1
Implicit MLE: Backpropagating Through Discrete Exponential Family DistributionsCode1
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-CutCode1
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing SystemCode1
Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approachCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
ASP: Learn a Universal Neural Solver!Code1
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