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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 876–900 of 1277 papers

TitleStatusHype
Differentiable TAN Structure Learning for Bayesian Network ClassifiersCode0
A Survey on Reinforcement Learning for Combinatorial Optimization—0
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network—0
Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning—0
Learning (Re-)Starting Solutions for Vehicle Routing Problems—0
Revisiting Robust Model Fitting Using Truncated LossCode0
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning—0
Bayesian preference elicitation for multiobjective combinatorial optimization—0
Performance Analysis of Meta-heuristic Algorithms for a Quadratic Assignment Problem—0
Positive Semidefinite Matrix Factorization: A Connection with Phase Retrieval and Affine Rank MinimizationCode0
DeepCO: Offline Combinatorial Optimization Framework Utilizing Deep Learning—0
Adversarial Immunization for Certifiable Robustness on GraphsCode1
A Generative Graph Method to Solve the Travelling Salesman Problem—0
Curriculum learning for multilevel budgeted combinatorial problemsCode0
Learning Combined Set Covering and Traveling Salesman Problem—0
Belief Propagation Neural NetworksCode1
Learning to Read through Machine Teaching—0
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
Constrained Combinatorial Optimization with Reinforcement Learning—0
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design—0
Learning What to Defer for Maximum Independent SetsCode1
Logically Synthesized, Hardware-Accelerated, Restricted Boltzmann Machines for Combinatorial Optimization and Integer Factorization—0
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