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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

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Showing 271–280 of 1277 papers

TitleStatusHype
Bottleneck potentials in Markov Random Fields—0
A new neighborhood structure for job shop scheduling problems—0
A Dynamic Algorithm for the Longest Common Subsequence Problem using Ant Colony Optimization Technique—0
Boosting Combinatorial Problem Modeling with Machine Learning—0
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning—0
A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice—0
A new dog learns old tricks: RL finds classic optimization algorithms—0
Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms—0
Robust Metric Learning by Smooth Optimization—0
Decision-focused Graph Neural Networks for Combinatorial Optimization—0
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