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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 551–575 of 1277 papers

TitleStatusHype
Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems—0
Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint—0
Robust Bayesian Inference for Moving Horizon Estimation—0
Budget-Aware Sequential Brick Assembly with Efficient Constraint SatisfactionCode0
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-CutCode1
Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphs—0
How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem—0
Automatic and effective discovery of quantum kernelsCode0
Learning Obstacle-Avoiding Lattice Paths using Swarm Heuristics: Exploring the Bijection to Ordered Trees—0
Structured Q-learning For Antibody Design—0
Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?—0
The (Un)Scalability of Heuristic Approximators for NP-Hard Search ProblemsCode0
Cooperative coevolutionary hybrid NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective optimization—0
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules—0
One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint SatisfactionCode0
Combinatorial optimization solving by coherent Ising machines based on spiking neural networks—0
Evaluate Quantum Combinatorial Optimization for Distribution Network ReconfigurationCode0
Combining Gradients and Probabilities for Heterogeneous Approximation of Neural NetworksCode0
Causal Effect Identification in Uncertain Causal Networks—0
Neural Set Function Extensions: Learning with Discrete Functions in High DimensionsCode0
Solving the vehicle routing problem with deep reinforcement learning—0
Analysis of Quality Diversity Algorithms for the Knapsack Problem—0
JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System—0
Learning with Combinatorial Optimization Layers: a Probabilistic ApproachCode1
Annealed Training for Combinatorial Optimization on Graphs—0
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