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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 1151–1175 of 1277 papers

TitleStatusHype
Implementation of digital MemComputing using standard electronic componentsCode0
A Survey and Analysis of Evolutionary Operators for PermutationsCode0
Simulation Based Bayesian OptimizationCode0
How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman ProblemCode0
MGNN: Graph Neural Networks Inspired by Distance Geometry ProblemCode0
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial OptimizationCode0
Graph-SCP: Accelerating Set Cover Problems with Graph Neural NetworksCode0
Black-box Combinatorial Optimization using Models with Integer-valued MinimaCode0
Curriculum Learning for Cumulative Return MaximizationCode0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial OptimizationCode0
Graph Adversarial Immunization for Certifiable RobustnessCode0
Global Optimal Path-Based Clustering AlgorithmCode0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Revisiting Robust Model Fitting Using Truncated LossCode0
On Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and AnalysisCode0
Dynamic Programming on a Quantum Annealer: Solving the RBC ModelCode0
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization ProblemsCode0
Word-level Textual Adversarial Attacking as Combinatorial OptimizationCode0
Dynamic Learning of Sequential Choice Bandit Problem under Marketing FatigueCode0
Reinforcement Learning for Solving the Vehicle Routing ProblemCode0
DistrictNet: Decision-aware learning for geographical districtingCode0
Optimal Intermittent Particle FilterCode0
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman ProblemsCode0
LLMs for Cold-Start Cutting Plane Separator ConfigurationCode0
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