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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 876–900 of 1277 papers

TitleStatusHype
Learning to Order Graph Elements with Application to Multilingual Surface Realization—0
Learning to Propose Objects—0
Learning to Read through Machine Teaching—0
Learning to repeatedly solve routing problems—0
Learning to Retrieve Iteratively for In-Context Learning—0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
Learning to Search in Branch and Bound Algorithms—0
Learning to Solve an Order Fulfillment Problem in Milliseconds with Edge-Feature-Embedded Graph Attention—0
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach—0
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time—0
Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks—0
Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture—0
Learning with Local Search MCMC Layers—0
Learning with Submodular Functions: A Convex Optimization Perspective—0
Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleet—0
Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization—0
Leveraging Conflicting Constraints in Solving Vehicle Routing Problems—0
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem—0
LIAR: Leveraging Alignment (Best-of-N) to Jailbreak LLMs in Seconds—0
Linear Inverse Problems with Norm and Sparsity Constraints—0
Liner Shipping Network Design with Reinforcement Learning—0
Link Prediction with Untrained Message Passing Layers—0
LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning—0
LMask: Learn to Solve Constrained Routing Problems with Lazy Masking—0
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