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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 221230 of 1277 papers

TitleStatusHype
Differentiable Quadratic Optimization For The Maximum Independent Set ProblemCode0
Learning to Remove Cuts in Integer Linear ProgrammingCode0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling0
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
Link Prediction with Untrained Message Passing Layers0
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial OptimizationCode1
GOAL: A Generalist Combinatorial Optimization Agent LearningCode1
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial OptimizationCode0
A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over GraphsCode0
Learning to Retrieve Iteratively for In-Context Learning0
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