EmpiricalHessian

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def hessian(df: DiffFunction[DenseVector[Double]], x: DenseVector[Double], eps: Double = 1E-5)(implicit vs: VectorSpace[DenseVector[Double], Double], copy: CanCopy[DenseVector[Double]]): DenseMatrix[Double]

Calculate the Hessian using central differences
Calculate the Hessian using central differences
H_{i,j} = \lim_h -> 0 ((f'(x_{i} + h*e_{j}) - f'(x_{i} + h*e_{j}))/4*h + (f'(x_{j} + h*e_{i}) - f'(x_{j} + h*e_{i}))/4*h)
where e_{i} is the unit vector with 1 in the i^^th position and zeros elsewhere
df
differentiable function
x
the point we compute the hessian for
eps
a small value
returns
Approximate hessian matrix
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@HotSpotIntrinsicCandidate()
implicit def product[T, I]: linalg.operators.OpMulMatrix.Impl2[EmpiricalHessian[T], T, T]
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Deprecated
(Since version ) see corresponding Javadoc for more information.

Related Docs: class EmpiricalHessian | package optimize

object EmpiricalHessian

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

final def getClass(): Class[_]

def hashCode(): Int

def hessian(df: DiffFunction[DenseVector[Double]], x: DenseVector[Double], eps: Double = 1E-5)(implicit vs: VectorSpace[DenseVector[Double], Double], copy: CanCopy[DenseVector[Double]]): DenseMatrix[Double]

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

implicit def product[T, I]: linalg.operators.OpMulMatrix.Impl2[EmpiricalHessian[T], T, T]

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

def toString(): String

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

final def wait(arg0: Long): Unit

final def wait(): Unit

Deprecated Value Members

def finalize(): Unit

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