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io.github.mandar2812.dynaml.optimization

ConjugateGradient

Related Docs: class ConjugateGradient | package optimization

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object ConjugateGradient extends Serializable

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  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. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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

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  10. def hashCode(): Int

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  11. final def isInstanceOf[T0]: Boolean

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  12. val logger: Logger

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

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. def runCG(A: PartitionedMatrix, b: PartitionedVector, x: PartitionedVector, epsilon: Double, MAX_ITERATIONS: Int): PartitionedVector

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  17. def runCG(A: SparkBlockedMatrix, b: SparkBlockedVector, x: SparkBlockedVector, epsilon: Double, MAX_ITERATIONS: Int): SparkBlockedVector

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  18. def runCG(A: SparkPSDMatrix, b: SparkVector, x: SparkVector, epsilon: Double, MAX_ITERATIONS: Int): SparkVector

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    Solves for x in A.x = b (where A is symmetric +ve semi-definite SparkPSDMatrix) iteratively using the Conjugate Gradient algorithm.

  19. def runCG(A: DenseMatrix[Double], b: DenseVector[Double], x: DenseVector[Double], epsilon: Double, MAX_ITERATIONS: Int): DenseVector[Double]

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    Solves for x in A.x = b (where A is symmetric +ve semi-definite) iteratively using the Conjugate Gradient algorithm.

  20. def runMultiCG(A: DenseMatrix[Double], b: DenseMatrix[Double], x: DenseMatrix[Double], epsilon: Double, MAX_ITERATIONS: Int): DenseMatrix[Double]

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    Solves for X in A.X = B (where A is symmetric +ve semi-definite) iteratively using the Conjugate Gradient algorithm.

  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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