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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

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Showing 301–310 of 1277 papers

TitleStatusHype
DeepSimplex: Reinforcement Learning of Pivot Rules Improves the Efficiency of Simplex Algorithm in Solving Linear Programming Problems—0
Barriers for the performance of graph neural networks (GNN) in discrete random structures. A comment on~schuetz2022combinatorial,angelini2023modern,schuetz2023reply—0
An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm—0
An efficient algorithm for learning with semi-bandit feedback—0
Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints—0
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks—0
A Bayesian approach for prompt optimization in pre-trained language models—0
An Efficient Algorithm for Cooperative Semi-Bandits—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction—0
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