StochasticGradientDescent

abstract def initialHistory(f: StochasticDiffFunction[T], init: T): History

abstract def updateHistory(newX: T, newGrad: T, newVal: Double, f: StochasticDiffFunction[T], oldState: State): History

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def adjust(newX: T, newGrad: T, newVal: Double): (Double, T)

def adjustFunction(f: StochasticDiffFunction[T]): StochasticDiffFunction[T]

final def asInstanceOf[T0]: T0

def calculateObjective(f: StochasticDiffFunction[T], x: T, history: History): (Double, T)

def chooseDescentDirection(state: State, fn: StochasticDiffFunction[T]): T

def clone(): AnyRef

val convergenceCheck: ConvergenceCheck[T]

val defaultStepSize: Double

def determineStepSize(state: State, f: StochasticDiffFunction[T], dir: T): Double

Choose a step size scale for this iteration.

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def infiniteIterations(f: StochasticDiffFunction[T], state: State): Iterator[State]

def initialState(f: StochasticDiffFunction[T], init: T): State

final def isInstanceOf[T0]: Boolean

def iterations(f: StochasticDiffFunction[T], init: T): Iterator[State]

def logger: LazyLogger

val maxIter: Int

def minimize(f: StochasticDiffFunction[T], init: T): T

def minimizeAndReturnState(f: StochasticDiffFunction[T], init: T): State

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def takeStep(state: State, dir: T, stepSize: Double): T

Projects the vector x onto whatever ball is needed.

def toString(): String

implicit val vspace: NormedModule[T, Double]

final def wait(): Unit

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

final def wait(arg0: Long): Unit