class
ConjugateGradient[T, M] extends AnyRef
Instance Constructors
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new
ConjugateGradient(maxNormValue: Double = scala.Double.PositiveInfinity, maxIterations: Int = -1, normSquaredPenalty: Double = 0, tolerance: Double = 1.0E-5)(implicit space: MutableInnerProductSpace[T, Double], mult: BinaryOp[M, T, OpMulMatrix, T])
Value Members
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final
def
!=(arg0: AnyRef): Boolean
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: AnyRef): Boolean
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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def
minimize(a: T, B: M, initX: T): T
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def
minimize(a: T, B: M): T
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def
minimizeAndReturnResidual(a: T, B: M, initX: T): (T, T)
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
Inherited from AnyRef
Inherited from Any
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