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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 321–330 of 1277 papers

TitleStatusHype
Brain-inspired Chaotic Graph Backpropagation for Large-scale Combinatorial Optimization—0
DistrictNet: Decision-aware learning for geographical districtingCode0
Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence ModelCode0
SizeGS: Size-aware Compression of 3D Gaussians with Hierarchical Mixed Precision Quantization—0
XKV: Personalized KV Cache Memory Reduction for Long-Context LLM Inference—0
LIAR: Leveraging Alignment (Best-of-N) to Jailbreak LLMs in Seconds—0
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention—0
Scalable iterative pruning of large language and vision models using block coordinate descent—0
Approximation Algorithms for Combinatorial Optimization with PredictionsCode0
Large Language Models for Combinatorial Optimization of Design Structure Matrix—0
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