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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 901–950 of 1277 papers

TitleStatusHype
Deep Momentum Uncertainty Hashing—0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Cost-aware Feature Selection for IoT Device Classification—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing—0
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
Bayesian preference elicitation for multiobjective combinatorial optimization—0
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning—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
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
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization—0
Learning to Read through Machine Teaching—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
Constrained Combinatorial Optimization with Reinforcement Learning—0
Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design—0
Logically Synthesized, Hardware-Accelerated, Restricted Boltzmann Machines for Combinatorial Optimization and Integer Factorization—0
Application of Monte Carlo Tree Search in Periodic Schedule Design for Networked Control Systems—0
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network—0
Kalman Filter Based Multiple Person Head Tracking—0
Exact and heuristic methods for the discrete parallel machine scheduling location problem—0
Graph Minors Meet Machine Learning: the Power of Obstructions—0
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time—0
Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game—0
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions—0
Learning Combinatorial Solver for Graph Matching—0
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded SizeCode0
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection—0
Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking—0
A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks—0
MineReduce: an approach based on data mining for problem size reduction—0
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman ProblemsCode0
Natural evolution strategies and variational Monte CarloCode0
A Heuristic Based on Randomized Greedy Algorithms for the Clustered Shortest-Path Tree Problem—0
Enriching Documents with Compact, Representative, Relevant Knowledge GraphsCode0
Multi-layer local optima networks for the analysis of advanced local search-based algorithms—0
Versatile Black-Box Optimization—0
Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?—0
Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem—0
Reinforcement Learning to Optimize the Logistics Distribution Routes of Unmanned Aerial Vehicle—0
Devolutionary genetic algorithms with application to the minimum labeling Steiner tree problem—0
Joint User Pairing and Association for Multicell NOMA: A Pointer Network-based Approach—0
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