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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 231240 of 1277 papers

TitleStatusHype
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial OptimizationCode0
Blackout DIFUSCOCode0
Black-box Combinatorial Optimization using Models with Integer-valued MinimaCode0
MGNN: Graph Neural Networks Inspired by Distance Geometry ProblemCode0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Global Optimal Path-Based Clustering AlgorithmCode0
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly DetectionCode0
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial OptimizationCode0
Futureproof Static Memory PlanningCode0
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