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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 501–525 of 1277 papers

TitleStatusHype
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering ProblemCode0
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning—0
A Survey and Analysis of Evolutionary Operators for PermutationsCode0
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks—0
All-to-all reconfigurability with sparse and higher-order Ising machinesCode0
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach—0
An Expandable Machine Learning-Optimization Framework to Sequential Decision-Making—0
RIGA: A Regret-Based Interactive Genetic Algorithm—0
Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm for Solving 0/1 Knapsack Problems—0
Computing with Residue Numbers in High-Dimensional Representation—0
Pointer Networks with Q-Learning for Combinatorial Optimization—0
Temporal Sequencing of DocumentsCode0
Addressing The Knapsack Challenge Through Cultural Algorithm Optimization—0
Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
High-Dimensional Prediction for Sequential Decision Making—0
Interferometric Neural NetworksCode0
Neural Packing: from Visual Sensing to Reinforcement Learning—0
On permutation symmetries in Bayesian neural network posteriors: a variational perspective—0
Enhancing Column Generation by Reinforcement Learning-Based Hyper-Heuristic for Vehicle Routing and Scheduling Problems—0
Graph-SCP: Accelerating Set Cover Problems with Graph Neural NetworksCode0
GRASP: Accelerating Shortest Path Attacks via Graph Attention—0
Diversity from Human Feedback—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Oracle Efficient Algorithms for Groupwise Regret—0
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