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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 276–300 of 1277 papers

TitleStatusHype
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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