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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 901–925 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
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