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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 676–700 of 1277 papers

TitleStatusHype
Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?—0
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data—0
Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning—0
Learning for Robust Combinatorial Optimization: Algorithm and Application—0
Pretrained Cost Model for Distributed Constraint Optimization ProblemsCode0
Decision-Focused Learning: Through the Lens of Learning to RankCode1
Constrained Resource Allocation Problems in Communications: An Information-assisted Approach—0
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
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
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming SeedingCode1
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation—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
Active Learning Meets Optimized Item SelectionCode1
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