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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 971980 of 1277 papers

TitleStatusHype
Discrete graphical models -- an optimization perspective0
Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem0
Implementing a GPU-based parallel MAX-MIN Ant SystemCode0
Graph Ordering: Towards the Optimal by Learning0
Parameterized Complexity Analysis of Randomized Search Heuristics0
High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET0
Clustering Binary Data by Application of Combinatorial Optimization Heuristics0
Learning fine-grained search space pruning and heuristics for combinatorial optimization0
A Probability Density Theory for Spin-Glass Systems0
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov modelsCode0
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