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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 141150 of 1277 papers

TitleStatusHype
Matrix Encoding Networks for Neural Combinatorial OptimizationCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Noisy intermediate-scale quantum algorithm for semidefinite programmingCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
Self-Supervision is All You Need for Solving Rubik's CubeCode1
Implicit MLE: Backpropagating Through Discrete Exponential Family DistributionsCode1
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Solve routing problems with a residual edge-graph attention neural networkCode1
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