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Patrick Wendell resolved SPARK-1157. ------------------------------------ Resolution: Fixed Fix Version/s: 1.0.0 > L-BFGS Optimizer > ---------------- > > Key: SPARK-1157 > URL: https://issues.apache.org/jira/browse/SPARK-1157 > Project: Spark > Issue Type: New Feature > Reporter: DB Tsai > Assignee: DB Tsai > Fix For: 1.0.0 > > > L-BFGS (Limited-memory BFGS) is an optimization algorithm like BFGS which > uses an approximation to the inverse of Hessian matrix to steer its search > through the variable space, but where BFGS stores a dense nxn approximation > to the inverse Hessian, L-BFGS only stores a few vectors to represent the > approximation. > For high dimensional optimization problems, the Newton method or BFGS is not > applicable since the amount of memory needed to store the Hessian will grow > exponentially, while L-BFGS only stores couple vectors. > One of the use case can be training large-scale logistic regression with so > many features. > We'll use breeze implementation of L-BFGS. -- This message was sent by Atlassian JIRA (v6.2#6252)