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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 51–100 of 1277 papers

TitleStatusHype
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows—0
UniCO: Towards a Unified Model for Combinatorial Optimization Problems—0
Primal-dual algorithm for contextual stochastic combinatorial optimization—0
Unraveling the Rainbow: can value-based methods schedule?Code0
Entropy-Guided Sampling of Flat Modes in Discrete SpacesCode0
Integrating Column Generation and Large Neighborhood Search for Bus Driver Scheduling with Complex Break Constraints—0
Learning to Learn with Quantum Optimization via Quantum Neural Networks—0
QAOA Parameter Transferability for Maximum Independent Set using Graph Attention Networks—0
Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search—0
Application of the Brain Drain Optimization Algorithm to the N-Queens Problem—0
QAOA-PCA: Enhancing Efficiency in the Quantum Approximate Optimization Algorithm via Principal Component Analysis—0
PGU-SGP: A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling—0
A 10.8mW Mixed-Signal Simulated Bifurcation Ising Solver using SRAM Compute-In-Memory with 0.6us Time-to-Solution—0
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
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning—0
Futureproof Static Memory PlanningCode0
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial OptimizationCode1
Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems—0
Unsupervised Learning for Quadratic Assignment—0
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic ProgrammingCode0
Neural Combinatorial Optimization for Real-World RoutingCode1
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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