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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 776–800 of 1277 papers

TitleStatusHype
Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search—0
Solving the QAP by Two-Stage Graph Pointer Networks and Reinforcement Learning—0
Solving the Travelling Thief Problem based on Item Selection Weight and Reverse Order Allocation—0
Solving the vehicle routing problem with deep reinforcement learning—0
Sparse Optimization for Green Edge AI Inference—0
Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks with Stochastic Energy Harvesting—0
Low-rank combinatorial optimization and statistical learning by spatial photonic Ising machine—0
Statistical estimation for optimization problems on graphs—0
Stochastic Online Greedy Learning with Semi-bandit Feedbacks—0
Storage placement policy for minimizing frequency deviation: A combinatorial optimization approach—0
Story-oriented Image Selection and Placement—0
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks—0
STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization—0
Streaming Algorithms for News and Scientific Literature Recommendation: Submodular Maximization with a d-Knapsack Constraint—0
Streaming, Distributed Variational Inference for Bayesian Nonparametrics—0
Structural Self-adaptation for Decentralized Pervasive Intelligence—0
Structured Q-learning For Antibody Design—0
Submodular Functions: from Discrete to Continous Domains—0
Submodular Information Selection for Hypothesis Testing with Misclassification Penalties—0
Submodularity of a Set Label Disagreement Function—0
Sub-network Multi-objective Evolutionary Algorithm for Filter Pruning—0
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size—0
Support Vector Algorithms for Optimizing the Partial Area Under the ROC Curve—0
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems—0
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization—0
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