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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 12711277 of 1277 papers

TitleStatusHype
Learning with Submodular Functions: A Convex Optimization Perspective0
Ant Colony Optimization and Hypergraph Covering Problems0
Active Instance Sampling via Matrix Partition0
Combinatorial Network Optimization with Unknown Variables: Multi-Armed Bandits with Linear Rewards0
DIFFRAC: a discriminative and flexible framework for clustering0
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions0
Optimization by Simulated AnnealingCode0
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