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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

Showing 12611270 of 1277 papers

TitleStatusHype
Multi-task Representation Learning for Mixed Integer Linear ProgrammingCode0
Estimating the stability number of a random graph using convolutional neural networksCode0
Natural evolution strategies and variational Monte CarloCode0
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage PlanningCode0
Navigating Memory Construction by Global Pseudo-Task Simulation for Continual LearningCode0
Balanced Crossover Operators in Genetic AlgorithmsCode0
QAL-BP: An Augmented Lagrangian Quantum Approach for Bin PackingCode0
Learning to Perform Local Rewriting for Combinatorial OptimizationCode0
Travel the Same Path: A Novel TSP Solving StrategyCode0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
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