scalation.minima

IntegerTabuSearch

class IntegerTabuSearch extends AnyRef

This class performs tabu search to find minima of functions defined on integer vector domains (z^n). Tabu search will not re-visit points already deemed sub-optimal.

minimize f(x) subject to g(x) <= 0, x in Z^n

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Instance Constructors

  1. new IntegerTabuSearch(f: (VectorI) ⇒ Double, g: (VectorI) ⇒ Double = null, maxStep: Int = 5)

    f

    the objective function to be minimize (f maps an integer vector to a double)

    g

    the constraint function to be satisfied, if any

    maxStep

    the maximum/starting step size (make larger for larger domains)

Type Members

  1. type Vec_Func = (VectorI, Double)

    Pair consisting of an integer vector and its functional value (a double)

Value Members

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

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

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  3. final def ##(): Int

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

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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

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  9. def equals(arg0: Any): Boolean

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  10. def fg(x: VectorI): Double

    The objective function f re-scaled by a weighted penalty, if constrained.

    The objective function f re-scaled by a weighted penalty, if constrained.

    x

    the coordinate values of the currrent point

  11. def finalize(): Unit

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  12. final def getClass(): java.lang.Class[_]

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. def minNeighbor(x_f0: (VectorI, Double), i: Int, step: Int = 1): (VectorI, Double)

    Find a minimal neighbor of the current point x that is a distance step away.

    Find a minimal neighbor of the current point x that is a distance step away. Let x be the current point with y being a step down and x being a step up in dimension i. Recurse to handle all of the dimensions.

    x_f0

    the current pair (the point and its functional value)

    i

    the i-th dimension or coordinate (facilitates recursion)

    step

    examine points that are this far away

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def solve(x: VectorI): (VectorI, Double)

    Solve the minimization problem by repeatedly moving to a minimal neighbor until there is no improvement.

    Solve the minimization problem by repeatedly moving to a minimal neighbor until there is no improvement.

    x

    the starting point for the search

  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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