LBFGS

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def runLBFGS(data: RDD[(Double, Vector)], gradient: Gradient, updater: Updater, numCorrections: Int, convergenceTol: Double, maxNumIterations: Int, regParam: Double, initialWeights: Vector): (Vector, Array[Double])

Run Limited-memory BFGS (L-BFGS) in parallel.
Run Limited-memory BFGS (L-BFGS) in parallel. Averaging the subgradients over different partitions is performed using one standard spark map-reduce in each iteration.
data
- Input data for L-BFGS. RDD of the set of data examples, each of the form (label, [feature values]).
gradient
- Gradient object (used to compute the gradient of the loss function of one single data example)
updater
- Updater function to actually perform a gradient step in a given direction.
numCorrections
- The number of corrections used in the L-BFGS update.
convergenceTol
- The convergence tolerance of iterations for L-BFGS which is must be nonnegative. Lower values are less tolerant and therefore generally cause more iterations to be run.
maxNumIterations
- Maximal number of iterations that L-BFGS can be run.
regParam
- Regularization parameter
returns
A tuple containing two elements. The first element is a column matrix containing weights for every feature, and the second element is an array containing the loss computed for every iteration.
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Related Docs: class LBFGS | package optimization

object LBFGS extends Logging with Serializable

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def isTraceEnabled(): Boolean

def log: Logger

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def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

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def runLBFGS(data: RDD[(Double, Vector)], gradient: Gradient, updater: Updater, numCorrections: Int, convergenceTol: Double, maxNumIterations: Int, regParam: Double, initialWeights: Vector): (Vector, Array[Double])

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