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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
Application of Monte Carlo Tree Search in Periodic Schedule Design for Networked Control Systems—0
Learning the Travelling Salesperson Problem Requires Rethinking GeneralizationCode1
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
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
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
Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood SearchCode1
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
Exploring the Loss Landscape in Neural Architecture SearchCode1
A Heuristic Based on Randomized Greedy Algorithms for the Clustered Shortest-Path Tree Problem—0
A Learning-based Iterative Method for Solving Vehicle Routing ProblemsCode1
Enriching Documents with Compact, Representative, Relevant Knowledge GraphsCode0
Versatile Black-Box Optimization—0
Multi-layer local optima networks for the analysis of advanced local search-based algorithms—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
Ants can orienteer a thief in their robberyCode0
Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs—0
Rethinking Differentiable Search for Mixed-Precision Neural NetworksCode1
A General Large Neighborhood Search Framework for Solving Integer Linear Programs—0
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Diseño e implementación de una meta-heurística multi-poblacional de optimización combinatoria enfocada a la resolución de problemas de asignación de rutas a vehículos—0
Segmentation and Optimal Region Selection of Physiological Signals using Deep Neural Networks and Combinatorial Optimization—0
Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method—0
Reinforcement Learning for Combinatorial Optimization: A Survey—0
A Bayesian algorithm for retrosynthesisCode1
Ising-based Consensus Clustering on Specialized Hardware—0
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective—0
The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery—0
Sparse Optimization for Green Edge AI Inference—0
Learning Interpretable Error Functions for Combinatorial Optimization Problem ModelingCode0
Incremental Sampling Without Replacement for Sequence ModelsCode1
Learn to Design the Heuristics for Vehicle Routing ProblemCode1
Constrained Multiagent Rollout and Multidimensional Assignment with the Auction Algorithm—0
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives—0
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
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