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Optimizing Optimization: Scalable Convex Programming with Proximal Operators
2018
Convex optimization has developed a wide variety of useful tools critical to many applications in machine learning. However, unlike linear and quadratic programming, general convex solvers have not yet reached sufficient maturity to fully decouple the convex programming model from the numerical algorithms required for implementation. Especially as datasets grow in size, there is a significant gap in speed and scalability between general solvers and specialized algorithms. This thesis addresses
doi:10.1184/r1/6720977.v1
fatcat:ukt3fhiulbcorct3itkjmdq37a