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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 76–100 of 1277 papers

TitleStatusHype
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware—0
FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data ClassificationCode1
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation—0
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms—0
Towards Constraint-Based Adaptive Hypergraph Learning for Solving Vehicle Routing: An End-to-End Solution—0
Neural Combinatorial Optimization via Preference Optimization—0
Combinatorial Optimization via LLM-driven Iterated Fine-tuning—0
Self-Supervised Penalty-Based Learning for Robust Constrained Optimization—0
Object Packing and Scheduling for Sequential 3D Printing: a Linear Arithmetic Model and a CEGAR-inspired Optimal Solver—0
Reheated Gradient-based Discrete Sampling for Combinatorial OptimizationCode0
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization ProblemsCode0
Learning to Reduce Search Space for Generalizable Neural Routing Solver—0
A2Perf: Real-World Autonomous Agents Benchmark—0
Lattice Protein Folding with Variational Annealing—0
Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization—0
Starjob: Dataset for LLM-Driven Job Shop SchedulingCode1
optimizn: a Python Library for Developing Customized Optimization Algorithms—0
Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc—0
Destroy and Repair Using Hyper Graphs for RoutingCode0
Synthesizing Composite Hierarchical Structure from Symbolic Music CorporaCode0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks—0
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization FormulationsCode0
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop SchedulingCode1
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage PlanningCode0
TSS GAZ PTP: Towards Improving Gumbel AlphaZero with Two-stage Self-play for Multi-constrained Electric Vehicle Routing Problems—0
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