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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 651660 of 1277 papers

TitleStatusHype
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial OptimizationCode0
L0Learn: A Scalable Package for Sparse Learning using L0 RegularizationCode1
Instance-wise algorithm configuration with graph neural networksCode1
Feature subset selection for Big Data via Chaotic Binary Differential Evolution under Apache Spark0
Heed the Noise in Performance Evaluations in Neural Architecture Search0
Exploring the Feature Space of TSP Instances Using Quality Diversity0
Yordle: An Efficient Imitation Learning for Branch and Bound0
MGNN: Graph Neural Networks Inspired by Distance Geometry ProblemCode0
Equivariant neural networks for recovery of Hadamard matrices0
ML4CO-KIDA: Knowledge Inheritance in Dataset AggregationCode1
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