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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
Learning Branching Heuristics from Graph Neural Networks0
Domain-Independent Dynamic Programming: Generic State Space Search for Combinatorial OptimizationCode1
Arbitrarily Large Labelled Random Satisfiability Formulas for Machine Learning Training0
Optimal Discrete Beamforming of RIS-Aided Wireless Communications: An Inner Product Maximization ApproachCode1
Deep Causal Learning: Representation, Discovery and Inference0
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization0
Feature Selection for Classification with QAOA0
Balancing Utility and Fairness in Submodular Maximization (Technical Report)Code0
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems0
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement LearningCode1
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