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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 1–50 of 1277 papers

TitleStatusHype
LRM-1B: Towards Large Routing Model—0
Large Language Models for Combinatorial Optimization: A Systematic Review—0
Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability—0
On Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and AnalysisCode0
HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization ChallengesCode2
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models—0
Synthesizing Min-Max Control Barrier Functions For Switched Affine Systems—0
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization—0
Large Language Models for Design Structure Matrix Optimization—0
Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning EnvironmentCode2
Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation—0
Systematic and Efficient Construction of Quadratic Unconstrained Binary Optimization Forms for High-order and Dense Interactions—0
HyColor: An Efficient Heuristic Algorithm for Graph Coloring—0
HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial OptimizationCode2
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy—0
Adam assisted Fully informed Particle Swarm Optimzation ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA)—0
Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLMCode0
Latent Guided Sampling for Combinatorial OptimizationCode0
EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization—0
Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search—0
Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization—0
Learning Distributions over Permutations and Rankings with Factorized Representations—0
Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees—0
LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning—0
Generalizable Heuristic Generation Through Large Language Models with Meta-Optimization—0
Efficient Optimization Accelerator Framework for Multistate Ising Problems—0
RedAHD: Reduction-Based End-to-End Automatic Heuristic Design with Large Language Models—0
Learning for Dynamic Combinatorial Optimization without Training Data—0
Structured Reinforcement Learning for Combinatorial Decision-MakingCode1
MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search—0
Demand Selection for VRP with Emission QuotaCode0
LMask: Learn to Solve Constrained Routing Problems with Lazy Masking—0
STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization—0
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms—0
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial OptimizationCode0
Learning from Algorithm Feedback: One-Shot SAT Solver Guidance with GNNs—0
A Quantum-Enhanced Power Flow and Optimal Power Flow based on Combinatorial Reformulation—0
Neural Quantum Digital Twins for Optimizing Quantum Annealing—0
Normalized Cut with Reinforcement Learning in Constrained Action Space—0
Learning with Local Search MCMC Layers—0
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language ModelsCode0
Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings—0
Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering—0
XX^t Can Be FasterCode0
A Generative Neural Annealer for Black-Box Combinatorial Optimization—0
Preference Optimization for Combinatorial Optimization Problems—0
Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem—0
Lagrange Oscillatory Neural Networks for Constraint Satisfaction and OptimizationCode0
Exact Spin Elimination in Ising Hamiltonians and Energy-Based Machine Learning—0
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