org.kramerlab.autoencoder.math.optimization

CgComparison

object CgComparison

Comparison of various re-implementations of conjugate gradient descent and a bunch of other, much simpler, optimization algorithms

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  1. class BananaFunction extends DifferentiableFunction

    Example 20.

  2. abstract class DifferentiableFunction extends (Vec) ⇒ (Double, Vec)

  3. type Vec = Array[Double]

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  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. def add(a: Array[Double], b: Array[Double]): Array[Double]

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

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

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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  12. final def getClass(): 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 main(args: Array[String]): Unit

  16. def mul(a: Array[Double], d: Double): Array[Double]

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

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  18. def normSq(a: Array[Double]): Double

  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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

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  22. def toString(): String

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  23. final def wait(): Unit

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

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

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