Class

breeze.optimize.linear

ConjugateGradient

Related Doc: package linear

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class ConjugateGradient[T, M] extends SerializableLogging

Solve argmin (a dot x + .5 * x dot (B * x) + .5 * normSquaredPenalty * (x dot x)) for x subject to norm(x) <= maxNormValue

Based on the code from "Trust Region Newton Method for Large-Scale Logistic Regression" * @author dlwh

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Instance Constructors

  1. new ConjugateGradient(maxNormValue: Double = Double.PositiveInfinity, maxIterations: Int = 1, normSquaredPenalty: Double = 0, tolerance: Double = 1E-5)(implicit space: MutableInnerProductVectorSpace[T, Double], mult: linalg.operators.OpMulMatrix.Impl2[M, T, T])

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Type Members

  1. case class State extends Product with Serializable

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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. def iterations(a: T, B: M, initX: T): Iterator[State]

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  13. def logger: LazyLogger

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  14. def minimize(a: T, B: M, initX: T): T

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  15. def minimize(a: T, B: M): T

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  16. def minimizeAndReturnResidual(a: T, B: M, initX: T): (T, T)

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    Returns the vector x and the vector r.

    Returns the vector x and the vector r. x is the minimizer, while r is the residual error (which may not be near zero because of the norm constraint.)

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

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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

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  21. def toString(): String

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  22. final def wait(): Unit

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

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

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Inherited from SerializableLogging

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