Papers Combinatorial Optimization
“Combinatorial Optimization” 태그가 달린 논문 1,277편 · 필터 해제
Large Language Models for Combinatorial Optimization: A Systematic Review
This systematic review explores the application of Large Language Models (LLMs) in Combinatorial Optimization (CO). We report our findings using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRI…
Combinatorial OptimizationLRM-1B: Towards Large Routing Model
Vehicle routing problems (VRPs) are central to combinatorial optimization with significant practical implications. Recent advancements in neural combinatorial optimization (NCO) have demonstrated promising results by lev…
Combinatorial OptimizationmodelHigher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability
We report a higher-order neuromorphic Ising machine that exhibits superior scalability compared to architectures based on quadratization, while also achieving state-of-the-art quality and reliability in solutions with co…
AllCombinatorial OptimizationOn Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and Analysis
In unsupervised combinatorial optimization (UCO), during training, one aims to have continuous decisions that are promising in a probabilistic sense for each training instance, which enables end-to-end training on initia…
Combinatorial OptimizationHeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges
Heuristic algorithms play a vital role in solving combinatorial optimization (CO) problems, yet traditional designs depend heavily on manual expertise and struggle to generalize across diverse instances. We introduce \te…
Combinatorial OptimizationGreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models
Although Large Vision Language Models (LVLMs) have demonstrated remarkable performance in image understanding tasks, their computational efficiency remains a significant challenge, particularly on resource-constrained de…
Combinatorial OptimizationComputational EfficiencyDiversitySynthesizing Min-Max Control Barrier Functions For Switched Affine Systems
We study the problem of synthesizing non-smooth control barrier functions (CBFs) for continuous-time switched affine systems. Switched affine systems are defined by a set of affine dynamical modes, wherein the control co…
Combinatorial OptimizationLarge Language Models for Design Structure Matrix Optimization
In complex engineering systems, the interdependencies among components or development activities are often modeled and analyzed using Design Structure Matrix (DSM). Reorganizing elements within a DSM to minimize feedback…
Combinatorial OptimizationMathematical ReasoningSynergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Combinatorial optimization problems are notoriously challenging due to their discrete structure and exponentially large solution space. Recent advances in deep reinforcement learning (DRL) have enabled the learning heuri…
Combinatorial OptimizationDeep Reinforcement LearningEvolutionary Algorithmsreinforcement-learning+1Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation
Recent deep reinforcement learning methods have achieved remarkable success in solving multi-objective combinatorial optimization problems (MOCOPs) by decomposing them into multiple subproblems, each associated with a sp…
Combinatorial OptimizationDeep Reinforcement LearningSystematic and Efficient Construction of Quadratic Unconstrained Binary Optimization Forms for High-order and Dense Interactions
Quantum Annealing (QA) can efficiently solve combinatorial optimization problems whose objective functions are represented by Quadratic Unconstrained Binary Optimization (QUBO) formulations. For broader applicability of …
Combinatorial OptimizationSolving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning Environment
The job shop scheduling problem is an NP-hard combinatorial optimization problem relevant to manufacturing and timetabling. Traditional approaches use priority dispatching rules based on simple heuristics. Recent work ha…
Combinatorial OptimizationImitation LearningJob Shop SchedulingSchedulingDomain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy
Ferroelectric polarization switching underpins the functional performance of a wide range of materials and devices, yet its dependence on complex local microstructural features renders systematic exploration by manual or…
Active LearningCombinatorial OptimizationHeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial Optimization
While Large Language Models (LLMs) have demonstrated significant advancements in reasoning and agent-based problem-solving, current evaluation methodologies fail to adequately assess their capabilities: existing benchmar…
Combinatorial OptimizationMemorizationHyColor: An Efficient Heuristic Algorithm for Graph Coloring
The graph coloring problem (GCP) is a classic combinatorial optimization problem that aims to find the minimum number of colors assigned to vertices of a graph such that no two adjacent vertices receive the same color. G…
Combinatorial OptimizationComputational EfficiencyAdam assisted Fully informed Particle Swarm Optimzation ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA)
The Quantum Approximate Optimization Algorithm (QAOA) is a prominent variational algorithm used for solving combinatorial optimization problems such as the Max-Cut problem. A key challenge in QAOA lies in efficiently ide…
Combinatorial OptimizationNavigateParameter PredictionIntelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLM
WiFi networks have achieved remarkable success in enabling seamless communication and data exchange worldwide. The IEEE 802.11be standard, known as WiFi 7, introduces Multi-Link Operation (MLO), a groundbreaking feature …
Combinatorial OptimizationLarge Language ModelLatent Guided Sampling for Combinatorial Optimization
Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging. Recent Neural Combinatorial Optimizatio…
Combinatorial OptimizationDrug DiscoveryReinforcement Learning (RL)Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search
The Pod Repositioning Problem (PRP) in Robotic Mobile Fulfillment Systems (RMFS) involves selecting optimal storage locations for pods returning from pick stations. This work presents an improved solution method that int…
Combinatorial OptimizationDeep Reinforcement LearningEALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization
Generating challenging instances is crucial for the evaluation and advancement of combinatorial optimization solvers. In this work, we introduce EALG (Evolutionary Adversarial Generation of Language Model-Guided Generato…
Combinatorial OptimizationLanguage ModelingLanguage Modelling