class LBFGS extends Optimizer with Logging
Class used to solve an optimization problem using Limited-memory BFGS. Reference: Wikipedia on Limited-memory BFGS
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- LBFGS.scala
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- def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector
Solve the provided convex optimization problem.
- def optimizeWithLossReturned(data: RDD[(Double, Vector)], initialWeights: Vector): (Vector, Array[Double])
- def setConvergenceTol(tolerance: Double): LBFGS.this.type
Set the convergence tolerance of iterations for L-BFGS.
Set the convergence tolerance of iterations for L-BFGS. Default 1E-6. Smaller value will lead to higher accuracy with the cost of more iterations. This value must be nonnegative. Lower convergence values are less tolerant and therefore generally cause more iterations to be run.
- def setGradient(gradient: Gradient): LBFGS.this.type
Set the gradient function (of the loss function of one single data example) to be used for L-BFGS.
- def setNumCorrections(corrections: Int): LBFGS.this.type
Set the number of corrections used in the LBFGS update.
Set the number of corrections used in the LBFGS update. Default 10. Values of numCorrections less than 3 are not recommended; large values of numCorrections will result in excessive computing time. numCorrections must be positive, and values from 4 to 9 are generally recommended.
- def setNumIterations(iters: Int): LBFGS.this.type
Set the maximal number of iterations for L-BFGS.
Set the maximal number of iterations for L-BFGS. Default 100.
- def setRegParam(regParam: Double): LBFGS.this.type
Set the regularization parameter.
Set the regularization parameter. Default 0.0.
- def setUpdater(updater: Updater): LBFGS.this.type
Set the updater function to actually perform a gradient step in a given direction.
Set the updater function to actually perform a gradient step in a given direction. The updater is responsible to perform the update from the regularization term as well, and therefore determines what kind or regularization is used, if any.
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(Since version 9)