c

scalation.minima

GradientDescent

class GradientDescent extends Minimizer with Error

The GradientDescent class solves unconstrained Non-Linear Programming (NLP) problems using the Gradient Descent algorithm. Given a function 'f' and a starting point 'x0', the algorithm computes the gradient and takes steps in the opposite direction. The algorithm iterates until it converges. The class assumes that partial derivative functions are not available unless explicitly given via the 'setDerivatives' method.

dir_k = -gradient (x)

minimize f(x)

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Error, Minimizer, AnyRef, Any
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Visibility
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Instance Constructors

  1. new GradientDescent(f: FunctionV2S, exactLS: Boolean = true)

    f

    the vector-to-scalar objective function

    exactLS

    whether to use exact (e.g., GoldenLS) or inexact (e.g., WolfeLS) Line Search

Type Members

  1. type Pair = (VectorD, VectorD)
    Definition Classes
    Minimizer

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val EPSILON: Double
    Attributes
    protected
    Definition Classes
    Minimizer
  5. val MAX_ITER: Int
    Attributes
    protected
    Definition Classes
    Minimizer
  6. val STEP: Double
    Attributes
    protected
    Definition Classes
    Minimizer
  7. val TOL: Double
    Attributes
    protected
    Definition Classes
    Minimizer
  8. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  9. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  12. def fg(x: VectorD): Double

    The objective function 'f' plus a weighted penalty based on the constraint function 'g'.

    The objective function 'f' plus a weighted penalty based on the constraint function 'g'. Override for constrained optimization and ignore for unconstrained optimization.

    x

    the coordinate values of the current point

    Definition Classes
    Minimizer
  13. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  14. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  15. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  16. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  17. def lineSearch(x: VectorD, dir: VectorD, step: Double = STEP): Double

    Perform an exact 'GoldenSectionLS' or inexact 'WolfeLS' line search.

    Perform an exact 'GoldenSectionLS' or inexact 'WolfeLS' line search. Search in direction 'dir', returning the distance 'z' to move in that direction.

    x

    the current point

    dir

    the direction to move in

    step

    the initial step size

    Definition Classes
    GradientDescent → Minimizer
  18. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  20. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  21. def setDerivatives(partials: Array[FunctionV2S]): Unit

    Set the partial derivative functions.

    Set the partial derivative functions. If these functions are available, they are more efficient and more accurate than estimating the values using difference quotients (the default approach).

    partials

    the array of partial derivative functions

  22. def solve(x0: VectorD, step: Double = STEP, toler: Double = EPSILON): VectorD

    Solve the Non-Linear Programming (NLP) problem using the Gradient Descent algorithm.

    Solve the Non-Linear Programming (NLP) problem using the Gradient Descent algorithm.

    x0

    the starting point

    step

    the initial step size

    toler

    the tolerance

    Definition Classes
    GradientDescent → Minimizer
  23. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  24. def toString(): String
    Definition Classes
    AnyRef → Any
  25. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  26. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
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    @throws( ... ) @native()
  27. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from Error

Inherited from Minimizer

Inherited from AnyRef

Inherited from Any

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