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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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Instance Constructors

  1. new LBFGS(gradient: Gradient, updater: Updater)

    gradient

    Gradient function to be used.

    updater

    Updater to be used to update weights after every iteration.

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  3. final def ==(arg0: Any): Boolean
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  5. def clone(): AnyRef
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  8. def finalize(): Unit
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  9. final def getClass(): Class[_ <: AnyRef]
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  10. def hashCode(): Int
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  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  13. final def isInstanceOf[T0]: Boolean
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  14. def isTraceEnabled(): Boolean
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  15. def log: Logger
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  16. def logDebug(msg: => String, throwable: Throwable): Unit
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  17. def logDebug(msg: => String): Unit
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  18. def logError(msg: => String, throwable: Throwable): Unit
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  19. def logError(msg: => String): Unit
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  20. def logInfo(msg: => String, throwable: Throwable): Unit
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  21. def logInfo(msg: => String): Unit
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  22. def logName: String
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  23. def logTrace(msg: => String, throwable: Throwable): Unit
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  24. def logTrace(msg: => String): Unit
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  25. def logWarning(msg: => String, throwable: Throwable): Unit
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  26. def logWarning(msg: => String): Unit
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  27. final def ne(arg0: AnyRef): Boolean
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  28. final def notify(): Unit
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  29. final def notifyAll(): Unit
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  30. def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector

    Solve the provided convex optimization problem.

    Solve the provided convex optimization problem.

    Definition Classes
    LBFGS → Optimizer
  31. def optimizeWithLossReturned(data: RDD[(Double, Vector)], initialWeights: Vector): (Vector, Array[Double])
  32. 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.

  33. 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.

  34. 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.

  35. 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.

  36. def setRegParam(regParam: Double): LBFGS.this.type

    Set the regularization parameter.

    Set the regularization parameter. Default 0.0.

  37. 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.

  38. final def synchronized[T0](arg0: => T0): T0
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  39. def toString(): String
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  40. final def wait(): Unit
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  41. final def wait(arg0: Long, arg1: Int): Unit
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  42. final def wait(arg0: Long): Unit
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Inherited from Logging

Inherited from Optimizer

Inherited from Serializable

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