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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
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
Implementing a GPU-based parallel MAX-MIN Ant SystemCode0
Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLMCode0
Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence ModelCode0
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet ControlCode0
Learning Combinatorial Optimization Algorithms over GraphsCode0
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial OptimizationCode0
MGNN: Graph Neural Networks Inspired by Distance Geometry ProblemCode0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Graph-SCP: Accelerating Set Cover Problems with Graph Neural NetworksCode0
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