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scalation.minima

DualSimplexTest

object DualSimplexTest extends App

The DualSimplexTest object is used to test the DualSimplex class.

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  1. DualSimplexTest
  2. App
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Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
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    Definition Classes
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  4. final def args: Array[String]
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    protected
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  5. final def asInstanceOf[T0]: T0
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    Any
  6. def clone(): AnyRef
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    protected[lang]
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: AnyRef): Boolean
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  9. final val executionStart: Long
    Definition Classes
    App
  10. final def getClass(): Class[_ <: AnyRef]
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    @native() @HotSpotIntrinsicCandidate()
  11. def hashCode(): Int
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    @native() @HotSpotIntrinsicCandidate()
  12. final def isInstanceOf[T0]: Boolean
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  13. final def main(args: Array[String]): Unit
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    App
  14. final def ne(arg0: AnyRef): Boolean
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    AnyRef
  15. final def notify(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  16. final def notifyAll(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  17. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  18. def test(a: MatrixD, b: VectorD, c: VectorD, x_B: Array[Int]): Unit

    Test the Dual Simplex Algorithm for solving Linear Programming problems.

    Test the Dual Simplex Algorithm for solving Linear Programming problems.

    a

    the constraint matrix

    b

    the limit/RHS vector

    c

    the cost vector

  19. def test1(): Unit

    Test case 1: Initialize matrix 'a', vectors 'b' and 'c', and optionally the basis 'x_B'.

    Test case 1: Initialize matrix 'a', vectors 'b' and 'c', and optionally the basis 'x_B'. For Dual Simplex, matrix 'a' and vector 'c' are not augmented. -------------------------------------------------------------------------- Minimize z = 2x_0 + 3x_1 + 4x_2 Subject to 1x_0 + 2x_1 + 1x_2 >= 3 2x_0 - 1x_1 + 3x_2 >= 4 where z is the objective variable and x is the decision vector. Since constraints are >=, multiply by rows by -1 -------------------------------------------------------------------------- Dual Solution: x = (11/5, 2/5), x_B = (0, 1), f = 28/5

    See also

    Linear Programming and Network Flows, Example 6.6

  20. def toString(): String
    Definition Classes
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  21. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  22. final def wait(arg0: Long): Unit
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  23. final def wait(): Unit
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    @throws(classOf[java.lang.InterruptedException])

Deprecated Value Members

  1. def delayedInit(body: => Unit): Unit
    Definition Classes
    App → DelayedInit
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    @deprecated
    Deprecated

    (Since version 2.11.0) the delayedInit mechanism will disappear

  2. def finalize(): Unit
    Attributes
    protected[lang]
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    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

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