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