Minimizer

abstract def minimize[V <: VectorSpace[V]](f: DifferentiableFunction[V], start: V, progressObservers: List[Observer[V]]): V

Finds a local minimum for a differentiable function f using it's values and gradient.

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

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

def manimize[V <: VectorSpace[V]](f: DifferentiableFunction[V], start: V): V

def minimize[V <: VectorSpace[V], Fitness](f: DifferentiableFunction[V], start: V, terminationCriterion: TerminationCriterion[V, (Int, Int)], resultSelector: ResultSelector[V, Fitness], progressObservers: List[Observer[V]])(implicit arg0: Ordering[Fitness]): V

If implemented, this minimization method can be used in order to use the minimization process as a source of possible candidate solutions, where the actual solution is not the one that minimizes f, but some other point, that maximizes some other fitness function.

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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