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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

Showing 5175 of 1277 papers

TitleStatusHype
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial OptimizationCode1
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time WindowsCode1
A Bayesian algorithm for retrosynthesisCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Discovering Dynamic Causal Space for DAG Structure LearningCode1
ASP: Learn a Universal Neural Solver!Code1
Denoising Autoencoders for fast Combinatorial Black Box OptimizationCode1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Attention, Learn to Solve Routing Problems!Code1
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
Active Learning Meets Optimized Item SelectionCode1
A Learning-based Iterative Method for Solving Vehicle Routing ProblemsCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
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