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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 251–300 of 1277 papers

TitleStatusHype
Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach—0
ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps—0
Annealed Mean Field Descent Is Highly Effective for Quadratic Unconstrained Binary Optimization—0
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application—0
Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms—0
Futureproof Static Memory PlanningCode0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning—0
Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems—0
Unsupervised Learning for Quadratic Assignment—0
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware—0
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic ProgrammingCode0
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms—0
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation—0
Towards Constraint-Based Adaptive Hypergraph Learning for Solving Vehicle Routing: An End-to-End Solution—0
Combinatorial Optimization via LLM-driven Iterated Fine-tuning—0
Neural Combinatorial Optimization via Preference Optimization—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
Learning to Reduce Search Space for Generalizable Neural Routing Solver—0
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization ProblemsCode0
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
optimizn: a Python Library for Developing Customized Optimization Algorithms—0
Destroy and Repair Using Hyper Graphs for RoutingCode0
Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc—0
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
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
Planning of Heuristics: Strategic Planning on Large Language Models with Monte Carlo Tree Search for Automating Heuristic Optimization—0
CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models—0
GraphThought: Graph Combinatorial Optimization with Thought Generation—0
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics—0
Improving Existing Optimization Algorithms with LLMs—0
Self-Evaluation for Job-Shop Scheduling—0
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning—0
Currency Arbitrage Optimization using Quantum Annealing, QAOA and Constraint Mapping—0
Blackout DIFUSCOCode0
Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set—0
Learning-Based TSP-Solvers Tend to Be Overly Greedy—0
Regularized Langevin Dynamics for Combinatorial OptimizationCode0
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms—0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver—0
Making Sense Of Distributed Representations With Activation Spectroscopy—0
PSO and the Traveling Salesman Problem: An Intelligent Optimization Approach—0
Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization—0
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