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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 1001–1010 of 1277 papers

TitleStatusHype
A topological analysis of the space of recipes—0
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem—0
Attention Round for Post-Training Quantization—0
A Tutorial about Random Neural Networks in Supervised Learning—0
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing—0
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems—0
A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks—0
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs—0
A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based Approaches for E-GTSP—0
Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design—0
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