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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 11411150 of 1277 papers

TitleStatusHype
Diversity from Human Feedback0
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize0
Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms0
Duality between Feature Selection and Data Clustering0
D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysis0
Dynamic Algorithms for Matroid Submodular Maximization0
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization0
Dynamic Assortment Optimization with Changing Contextual Information0
Dynamic Feature Selection for Dependency Parsing0
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition0
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