Object/Class

com.tencent.angel.ml.optimizer.admm

ADMM

Related Docs: class ADMM | package admm

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object ADMM

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Type Members

  1. type T = SparseDoubleVector

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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. final def asInstanceOf[T0]: T0

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  5. def calcLocalLoss(data: DataBlock[LabeledData], localModel: LocalModel): Double

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  6. def clone(): AnyRef

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  7. def computeZ(z: T, model: SparseLRModel, regParam: Double, rho: Double, N: Int): Unit

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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 finalize(): Unit

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

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  12. def getGlobalLoss(data: DataBlock[LabeledData], model: SparseLRModel, localModel: LocalModel, z: T, regParam: Double): Double

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  13. def getT(z: T, model: SparseLRModel, localModel: LocalModel, localIndex: LocalIndex): Double

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  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 runADMM(data: DataBlock[LabeledData], model: SparseLRModel)(regParam: Double, rho: Double, N: Int, threadNum: Int, maxNumIterations: Int = 20, primalTol: Double = 1e-5, dualTol: Double = 1e-5)(implicit ctx: TaskContext, globalMetrics: GlobalMetrics): (Array[Double], T)

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  20. def split(data: DataBlock[LabeledData], splitNum: Int): Array[DataBlock[LabeledData]]

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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. def updateUandX(z: T, data: DataBlock[LabeledData], localModel: LocalModel, localIndex: LocalIndex, rho: Double, threadNum: Int): Unit

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  24. def updateW(model: SparseLRModel, localModel: LocalModel, localIndex: LocalIndex): Unit

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  25. final def wait(): Unit

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

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

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