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com.tencent.angel.ml.optimizer.admm

ADMM

Related Docs: object ADMM | package admm

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class ADMM extends AnyRef

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Instance Constructors

  1. new ADMM()

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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 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. final def ne(arg0: AnyRef): Boolean

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  13. final def notify(): Unit

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  14. final def notifyAll(): Unit

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  15. def setDualTol(tolenrance: Double): ADMM.this.type

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    Set the dual convergence tolerance of iterations.

  16. def setN(N: Int): ADMM.this.type

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  17. def setNumIterations(iterNum: Int): ADMM.this.type

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  18. def setPrimalTol(tolenrance: Double): ADMM.this.type

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    Set the primal convergence tolerance of iterations.

  19. def setRegParam(regParam: Double): ADMM.this.type

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    set the regularization parameter.

    set the regularization parameter. Default 0.0.

  20. def setRho(rho: Double): ADMM.this.type

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    Set rho which is the augmented Lagrangian parameter.

    Set rho which is the augmented Lagrangian parameter. kappa = regParam / (rho * numSubModels), if the absolute value of element in model is less than kappa, this element will be assigned to zero. So kappa should be less than 0.01 or 0.001.

  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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