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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 151–175 of 1277 papers

TitleStatusHype
LIAR: Leveraging Alignment (Best-of-N) to Jailbreak LLMs in Seconds—0
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention—0
Scalable iterative pruning of large language and vision models using block coordinate descent—0
Approximation Algorithms for Combinatorial Optimization with PredictionsCode0
Large Language Models for Combinatorial Optimization of Design Structure Matrix—0
Design And Optimization Of Multi-rendezvous Manoeuvres Based On Reinforcement Learning And Convex Optimization—0
Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP SolversCode1
Liner Shipping Network Design with Reinforcement Learning—0
Neuro-Symbolic Rule Lists—0
MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning—0
Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleet—0
Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems—0
A Random-Key Optimizer for Combinatorial Optimization—0
Neural Networks and (Virtual) Extended Formulations—0
Deep memetic models for combinatorial optimization problems: application to the tool switching problem—0
Towards Geometry-Preserving Reductions Between Constraint Satisfaction Problems (and other problems in NP)—0
Multi-IRS Enhanced Wireless Coverage: Deployment Optimization Based on Large-Scale Channel Knowledge—0
Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization—0
Permutation Picture of Graph Combinatorial Optimization Problems—0
Offline reinforcement learning for job-shop scheduling problems—0
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines—0
Selection of Filters for Photonic Crystal Spectrometer Using Domain-Aware Evolutionary Algorithms—0
LLMOPT: Learning to Define and Solve General Optimization Problems from ScratchCode2
Initialization Method for Factorization Machine Based on Low-Rank Approximation for Constructing a Corrected Approximate Ising Model—0
Unsupervised Training of Diffusion Models for Feasible Solution Generation in Neural Combinatorial Optimization—0
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