Limited-memory BFGS - WikipediaLimited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the collection of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory.[1] It is a popular algorithm for parameter estimation in machine learning.[2][3] The algorithm's target problem is to minimize over unconstrained values of the real-vector wh
pyspark.mllib package — PySpark 2.1.0 documentation
