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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 611620 of 1277 papers

TitleStatusHype
Differentiable Scaffolding Tree for Molecule Optimization0
Differentiable Scaffolding Tree for Molecular Optimization0
Automatic Rank Selection for High-Speed Convolutional Neural Network0
An Approximation Algorithm for Risk-averse Submodular Optimization0
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling0
A Bayesian approach for prompt optimization in pre-trained language models0
Differentiable Greedy Networks0
Devolutionary genetic algorithms with application to the minimum labeling Steiner tree problem0
Detecting Overlapping Temporal Community Structure in Time-Evolving Networks0
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property0
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