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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 541–550 of 1277 papers

TitleStatusHype
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
Efficient LDPC Decoding using Physical Computation—0
Boosting Combinatorial Problem Modeling with Machine Learning—0
Efficient correlation-based discretization of continuous variables for annealing machines—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
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network—0
Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack—0
Efficient Algorithms for Adversarial Contextual Learning—0
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