scalation.minima

GoldenSectionLS

Related Doc: package minima

class GoldenSectionLS extends LineSearch

This class performs a line search on f(x) to find a minimal value for f. It requires no derivatives and only one functional evaluation per iteration. A search is conducted from x1 (often 0) to xmax. A guess for xmax must be given, but can be made larger during the expansion phase, that occurs before the recursive golden section search is called. It works on scalar functions (see GoldenSectionLSTest). If starting with a vector function f(x), simply define a new function g(y) = x0 + direction * y (see GoldenSectionLSTest2).

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Instance Constructors

  1. new GoldenSectionLS(f: FunctionS2S)

    f

    the scalar objective function to minimize

Value Members

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

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  2. final def ##(): Int

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

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  4. val EPSILON: Double

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    LineSearch
  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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

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  11. def gsection(left: Boolean, x1: Double, x2: Double, x3: Double, f2: Double): Double

    A recursive golden section search requiring only one functional evaluation per call.

    A recursive golden section search requiring only one functional evaluation per call. It works by comparing two center points x2 (given) and x4 computed.

    left

    whether to search left (true) or right (false) side of last interval

    x1

    the left-most point

    x2

    the center point (.618 across for left and .382 across for right)

    x3

    the right-most point

    f2

    the functional value for the x2 center point

  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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    Any
  14. def lsearch(xmax: Double = 2.0, x1: Double = 0.0): Double

    Perform an exact Line Search (LS) using the Golden Search Algorithm.

    Perform an exact Line Search (LS) using the Golden Search Algorithm. Two phases are used: an expansion phase (moving the endpoint) to find a down-up pattern, followed by a traditional golden section search.

    xmax

    a rough guess for the right endpoint of the line search

    x1

    the left (smallest) anchor point for the search (usually 0)

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

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    AnyRef
  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. def printGolden: Unit

    Print the golden ratio and the golden section.

  19. def search(step: Double = 2.0): Double

    Perform an exact Line Search (LS) using the Golden Search Algorithm with defaults.

    Perform an exact Line Search (LS) using the Golden Search Algorithm with defaults.

    step

    the initial step size

    Definition Classes
    GoldenSectionLSLineSearch
  20. final def synchronized[T0](arg0: ⇒ T0): T0

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  21. def toString(): String

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  22. final def wait(): Unit

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

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

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Inherited from LineSearch

Inherited from AnyRef

Inherited from Any

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