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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 726–750 of 1277 papers

TitleStatusHype
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing—0
Evolutionary Construction of Perfectly Balanced Boolean Functions—0
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial OptimizationCode0
Feature subset selection for Big Data via Chaotic Binary Differential Evolution under Apache Spark—0
Exploring the Feature Space of TSP Instances Using Quality Diversity—0
Heed the Noise in Performance Evaluations in Neural Architecture Search—0
Yordle: An Efficient Imitation Learning for Branch and Bound—0
MGNN: Graph Neural Networks Inspired by Distance Geometry ProblemCode0
Equivariant neural networks for recovery of Hadamard matrices—0
Classical Simulation of Variational Quantum Classifiers using Tensor Rings—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
Recent Advances in Deep Learning for Routing Problems—0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
A Quadratic 0-1 Programming Approach for Word Sense Disambiguation—0
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size—0
Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure—0
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs—0
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks—0
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?—0
ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data—0
Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning—0
Learning for Robust Combinatorial Optimization: Algorithm and Application—0
Pretrained Cost Model for Distributed Constraint Optimization ProblemsCode0
Constrained Resource Allocation Problems in Communications: An Information-assisted Approach—0
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