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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 10811090 of 1277 papers

TitleStatusHype
Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems0
Convergence Acceleration of Markov Chain Monte Carlo-based Gradient Descent by Deep Unfolding0
Convergence and Running Time of Time-dependent Ant Colony Algorithms0
Cool-Fusion: Fuse Large Language Models without Training0
Cooperative coevolutionary hybrid NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective optimization0
COPS: Controlled Pruning Before Training Starts0
Cortical Processing with Thermodynamic-RAM0
Cost-aware Feature Selection for IoT Device Classification0
CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs0
Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach0
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