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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 11111120 of 1277 papers

TitleStatusHype
Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World0
The Exact Solution to Rank-1 L1-norm TUCKER2 DecompositionCode0
Deep Learning as a Mixed Convex-Combinatorial Optimization ProblemCode0
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference0
EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure0
Inference in Graphical Models via Semidefinite Programming Hierarchies0
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method0
INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis0
Protein design by multiobjective optimization: evolutionary and non-evolutionary approaches0
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications0
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