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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 351–375 of 1277 papers

TitleStatusHype
Characterization of Locality in Spin States and Forced Moves for Optimizations—0
Towards the Inferrence of Structural Similarity of Combinatorial Landscapes—0
Pointer Networks Trained Better via Evolutionary Algorithms—0
Biased Random-Key Genetic Algorithms: A Review—0
A Bayesian approach for prompt optimization in pre-trained language models—0
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
Variational Annealing on Graphs for Combinatorial OptimizationCode1
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
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization RegimeCode1
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
Temporal Sequencing of DocumentsCode0
Pointer Networks with Q-Learning for Combinatorial Optimization—0
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
Large Language Models as Evolutionary OptimizersCode1
High-Dimensional Prediction for Sequential Decision Making—0
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