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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 761770 of 1277 papers

TitleStatusHype
The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery0
The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 110
The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem0
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle0
The Influence of Local Search over Genetic Algorithms with Balanced Representations0
The Lovász ϑ function, SVMs and finding large dense subgraphs0
The Neural-Prediction based Acceleration Algorithm of Column Generation for Graph-Based Set Covering Problems0
Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization0
Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons0
Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization0
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