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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 141150 of 1277 papers

TitleStatusHype
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling ProblemsCode1
LLMs for Cold-Start Cutting Plane Separator ConfigurationCode0
Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks0
Brain-inspired Chaotic Graph Backpropagation for Large-scale Combinatorial Optimization0
Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptation0
DistrictNet: Decision-aware learning for geographical districtingCode0
Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence ModelCode0
XKV: Personalized KV Cache Memory Reduction for Long-Context LLM Inference0
SizeGS: Size-aware Compression of 3D Gaussians with Hierarchical Mixed Precision Quantization0
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