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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 376–400 of 1277 papers

TitleStatusHype
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial OptimizationCode0
Modeling Local Search Metaheuristics Using Markov Decision Processes—0
Cool-Fusion: Fuse Large Language Models without Training—0
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems—0
Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update—0
Estimating the stability number of a random graph using convolutional neural networksCode0
Generalizing and Unifying Gray-box Combinatorial Optimization Operators—0
PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT InferenceCode0
VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models—0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems—0
Differentiable Quadratic Optimization For The Maximum Independent Set ProblemCode0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling—0
Learning to Remove Cuts in Integer Linear ProgrammingCode0
Link Prediction with Untrained Message Passing Layers—0
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial OptimizationCode0
A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over GraphsCode0
Graph Neural Networks for Job Shop Scheduling Problems: A Survey—0
Learning to Retrieve Iteratively for In-Context Learning—0
Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization—0
A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks—0
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity AnalysisCode0
Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization—0
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning—0
Intertwining CP and NLP: The Generation of Unreasonably Constrained Sentences—0
A Benchmark for Maximum Cut: Towards Standardization of the Evaluation of Learned Heuristics for Combinatorial OptimizationCode0
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