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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 751–775 of 1277 papers

TitleStatusHype
Multidimensional Assignment Problem for multipartite entity resolution—0
Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization—0
Constrained Machine Learning: The Bagel Framework—0
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation—0
Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model—0
Solving Graph-based Public Goods Games with Tree Search and Imitation LearningCode0
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs—0
Reinforcement Learning Enhanced Explainer for Graph Neural Networks—0
Eliciting and Distinguishing Between Weak and Incomplete Preferences: Theory, Experiment and Computation—0
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization—0
SatNet: A Benchmark for Satellite Scheduling Optimization—0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
Reversible Action Design for Combinatorial Optimization with ReinforcementLearning—0
Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks—0
BiGrad: Differentiating through Bilevel Optimization Programming—0
Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning—0
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence—0
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle—0
Large Scale Diverse Combinatorial Optimization: ESPN Fantasy Football Player Trades—0
Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors—0
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social NetworksCode0
Sample Selection for Fair and Robust Training—0
Interpretable Decision Trees Through MaxSAT—0
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness—0
Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly—0
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