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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 851–875 of 1277 papers

TitleStatusHype
Learning Combined Set Covering and Traveling Salesman Problem—0
Learning Discrete Directed Acyclic Graphs via Backpropagation—0
Learning Distributions over Permutations and Rankings with Factorized Representations—0
Learning DNN networks using un-rectifying ReLU with compressed sensing application—0
Learning fine-grained search space pruning and heuristics for combinatorial optimization—0
Learning for Dynamic Combinatorial Optimization without Training Data—0
Learning for Robust Combinatorial Optimization: Algorithm and Application—0
Learning from Algorithm Feedback: One-Shot SAT Solver Guidance with GNNs—0
Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits—0
Learning Heuristics for the Maximum Clique Enumeration Problem Using Low Dimensional Representations—0
Learning Obstacle-Avoiding Lattice Paths using Swarm Heuristics: Exploring the Bijection to Ordered Trees—0
Interpretable Decision Trees Through MaxSAT—0
Learning Pseudo-Backdoors for Mixed Integer Programs—0
Learning (Re-)Starting Solutions for Vehicle Routing Problems—0
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs—0
Learning Self-Game-Play Agents for Combinatorial Optimization Problems—0
Learning the Quality of Machine Permutations in Job Shop Scheduling—0
Learning to Branch in Combinatorial Optimization with Graph Pointer Networks—0
Learning To Dive In Branch And Bound—0
Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks—0
Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model—0
Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location—0
Learning to Learn with Quantum Optimization via Quantum Neural Networks—0
Learning to Make Decisions via Submodular Regularization—0
Maximizing Influence with Graph Neural Networks—0
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