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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 301–325 of 1277 papers

TitleStatusHype
Efficient Combinatorial Optimization via Heat DiffusionCode0
Deep Reinforcement Learning for Modelling Protein Complexes—0
Ant Colony Sampling with GFlowNets for Combinatorial OptimizationCode2
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network PruningCode0
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem—0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
RouteExplainer: An Explanation Framework for Vehicle Routing ProblemCode1
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm—0
Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instancesCode0
Learning to Solve Job Shop Scheduling under UncertaintyCode2
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems—0
MMSR: Symbolic Regression is a Multi-Modal Information Fusion TaskCode1
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization—0
Box Facets and Cut Facets of Lifted Multicut Polytopes—0
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot GeneralizationCode1
Towards Principled Task Grouping for Multi-Task Learning—0
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities—0
PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization—0
Reasoning Algorithmically in Graph Neural Networks—0
Convergence Acceleration of Markov Chain Monte Carlo-based Gradient Descent by Deep Unfolding—0
RITFIS: Robust input testing framework for LLMs-based intelligent software—0
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution ShiftsCode0
Large Scale Constrained Clustering With Reinforcement Learning—0
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization—0
Assortment Planning with Sponsored Products—0
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