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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 521530 of 1277 papers

TitleStatusHype
Adam assisted Fully informed Particle Swarm Optimzation ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA)0
Gumbel-softmax Optimization: A Simple General Framework for Combinatorial Optimization Problems on Graphs0
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning0
D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation0
A Survey for Solving Mixed Integer Programming via Machine Learning0
Algoritmos Genéticos Aplicado ao Problema de Roteamento de Veículos0
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation0
A 2-approximation algorithm for the softwired parsimony problem on binary, tree-child phylogenetic networks0
Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization0
Hardness of Online Sleeping Combinatorial Optimization Problems0
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