scalation.minima

SteepestDescent

class SteepestDescent extends Minimizer with Error

This class solves unconstrained Non-Linear Programming (NLP) problems using the Steepest Descent algorithm. Given a function 'f' and a starting point 'x', 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 availble 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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Instance Constructors

  1. new SteepestDescent(f: (VectorD) ⇒ Double, 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

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any

  6. val EPSILON: Double

    Attributes
    protected
    Definition Classes
    Minimizer

  7. val MAX_ITER: Int

    Attributes
    protected
    Definition Classes
    Minimizer

  8. val STEP: Double

    Attributes
    protected
    Definition Classes
    Minimizer

  9. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  11. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  12. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  13. 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

  14. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  15. def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error

  16. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  17. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  19. 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
    SteepestDescent → Minimizer

  20. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  21. final def notify(): Unit

    Definition Classes
    AnyRef

  22. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  23. def setDerivatives(partials: Array[(VectorD) ⇒ Double]): 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

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

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

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

    x0

    the starting point

    step

    the initial step size

    toler

    the tolerence

    Definition Classes
    SteepestDescent → Minimizer

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

    Definition Classes
    AnyRef

  26. def toString(): String

    Definition Classes
    AnyRef → Any

  27. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  29. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Error

Inherited from Minimizer

Inherited from AnyRef

Inherited from Any

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