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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 426–450 of 1277 papers

TitleStatusHype
Causal Effect Identification in Uncertain Causal Networks—0
Entity Summarization: State of the Art and Future Challenges—0
Causal Discovery with Reinforcement Learning—0
Deep Learning of Graph Matching—0
Equivariant neural networks for recovery of Hadamard matrices—0
Attention Round for Post-Training Quantization—0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training—0
ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps—0
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems—0
Assessing Distribution Network Flexibility via Reliability-based P-Q Area Segmentation—0
An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem—0
Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression—0
Estudo comparativo de meta-heurísticas para problemas de colorações de grafos—0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem—0
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization—0
Evaluation of bioinspired algorithms for the solution of the job scheduling problem—0
Evolutionary Approach for the Containers Bin-Packing Problem—0
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives—0
Evolutionary Construction of Perfectly Balanced Boolean Functions—0
Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits—0
Evolutionary RL for Container Loading—0
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise—0
Fast Hyperparameter Tuning for Ising Machines—0
Exact and Approximate Hierarchical Clustering Using A*—0
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem—0
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