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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 851–900 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
Learning to Order Graph Elements with Application to Multilingual Surface Realization—0
Learning to Propose Objects—0
Learning to Read through Machine Teaching—0
Learning to repeatedly solve routing problems—0
Learning to Retrieve Iteratively for In-Context Learning—0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
Learning to Search in Branch and Bound Algorithms—0
Learning to Solve an Order Fulfillment Problem in Milliseconds with Edge-Feature-Embedded Graph Attention—0
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach—0
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time—0
Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks—0
Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture—0
Learning with Local Search MCMC Layers—0
Learning with Submodular Functions: A Convex Optimization Perspective—0
Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleet—0
Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization—0
Leveraging Conflicting Constraints in Solving Vehicle Routing Problems—0
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem—0
LIAR: Leveraging Alignment (Best-of-N) to Jailbreak LLMs in Seconds—0
Linear Inverse Problems with Norm and Sparsity Constraints—0
Liner Shipping Network Design with Reinforcement Learning—0
Link Prediction with Untrained Message Passing Layers—0
LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning—0
LMask: Learn to Solve Constrained Routing Problems with Lazy Masking—0
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