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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 1101–1125 of 1277 papers

TitleStatusHype
NeuroPrim: An Attention-based Model for Solving NP-hard Spanning Tree ProblemsCode0
Submodular Batch Selection for Training Deep Neural NetworksCode0
Deep Learning Chromatic and Clique Numbers of GraphsCode0
Learning Geometric Combinatorial Optimization Problems using Self-attention and Domain KnowledgeCode0
Efficient Combinatorial Optimization via Heat DiffusionCode0
Self-Adaptive Ising Machines for Constrained OptimizationCode0
WardropNet: Traffic Flow Predictions via Equilibrium-Augmented LearningCode0
Self-averaging of digital memcomputing machinesCode0
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial OptimizationCode0
Deep Learning as a Mixed Convex-Combinatorial Optimization ProblemCode0
The (Un)Scalability of Heuristic Approximators for NP-Hard Search ProblemsCode0
USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization ProblemsCode0
Learning General Policies from Small Examples Without SupervisionCode0
Structural Causal Models Reveal Confounder Bias in Linear Program ModellingCode0
LeadCache: Regret-Optimal Caching in NetworksCode0
Learning Heuristics over Large Graphs via Deep Reinforcement LearningCode0
Interferometric Neural NetworksCode0
Graph Neural Networks for the Offline Nanosatellite Task Scheduling ProblemCode0
Blackout DIFUSCOCode0
Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLMCode0
Sub-universal variational circuits for combinatorial optimization problemsCode0
Decision-Focused Learning to Predict Action Costs for PlanningCode0
Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instancesCode0
Pruning Edges and Gradients to Learn Hypergraphs from Larger SetsCode0
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing SolverCode0
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