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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 10211030 of 1277 papers

TitleStatusHype
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling0
Biased Random-Key Genetic Algorithms: A Review0
BiGrad: Differentiating through Bilevel Optimization Programming0
Binary matrix factorization on special purpose hardware0
Binary sequence set optimization for CDMA applications via mixed-integer quadratic programming0
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning0
Boosting Combinatorial Problem Modeling with Machine Learning0
Bottleneck potentials in Markov Random Fields0
Brain-inspired Chaotic Graph Backpropagation for Large-scale Combinatorial Optimization0
Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization0
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