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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 801810 of 1277 papers

TitleStatusHype
Simulating the Effects of Eco-Friendly Transportation Selections for Air Pollution Reduction0
DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problem0
A new neighborhood structure for job shop scheduling problems0
Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack0
High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware0
Optimization Networks for Integrated Machine Learning0
Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity0
Maximizing Influence with Graph Neural Networks0
On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization0
Deep Learning Chromatic and Clique Numbers of GraphsCode0
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