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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 10711080 of 1277 papers

TitleStatusHype
Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks0
Constrained Combinatorial Optimization with Reinforcement Learning0
Constrained Machine Learning: The Bagel Framework0
Constrained Multiagent Rollout and Multidimensional Assignment with the Auction Algorithm0
Constrained Resource Allocation Problems in Communications: An Information-assisted Approach0
Constraint Programming to Discover One-Flip Local Optima of Quadratic Unconstrained Binary Optimization Problems0
Constraints First: A New MDD-based Model to Generate Sentences Under Constraints0
Content Provider Dynamics and Coordination in Recommendation Ecosystems0
Context-Aware Online Adaptation of Mixed Reality Interfaces0
Continuous Latent Search for Combinatorial Optimization0
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