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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 1–25 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
Large Language Models for Design Structure Matrix Optimization—0
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization—0
Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning EnvironmentCode2
Systematic and Efficient Construction of Quadratic Unconstrained Binary Optimization Forms for High-order and Dense Interactions—0
Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation—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
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