RegularizedOptimizer

abstract def optimize(nPoints: Long, ParamOutEdges: S, initialP: P): P

Solve the convex optimization problem.

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

var miniBatchFraction: Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var numIterations: Int

var regParam: Double

def setMiniBatchFraction(fraction: Double): RegularizedOptimizer.this.type

Set fraction of data to be used for each SGD iteration.

def setNumIterations(iters: Int): RegularizedOptimizer.this.type

Set the number of iterations for SGD.

def setRegParam(regParam: Double): RegularizedOptimizer.this.type

Set the regularization parameter.

def setStepSize(step: Double): RegularizedOptimizer.this.type

Set the initial step size of SGD for the first step.

var stepSize: Double

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit