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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 11511160 of 1277 papers

TitleStatusHype
MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms0
Parameter Learning for Log-supermodular Distributions0
How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods0
Pruning Random Forests for Prediction on a Budget0
Sample Complexity of Automated Mechanism Design0
On the performance of different mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems0
Local Perturb-and-MAP for Structured Prediction0
Support Vector Algorithms for Optimizing the Partial Area Under the ROC Curve0
Random-Key Cuckoo Search for the Travelling Salesman Problem0
Solving Optimization Problems by the Public Goods Game0
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