LBFGS

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29. 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

    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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