breeze/breeze.optimize.proximal/QuadraticMinimizer

QuadraticMinimizer

object QuadraticMinimizer
Companion
class
class Object
trait Matchable
class Any

Type members

Classlikes

case class Cost(H: DenseMatrix[Double], q: DenseVector[Double]) extends DiffFunction[DenseVector[Double]]

Value members

Concrete methods

def apply(rank: Int, constraint: Constraint, lambda: Double): QuadraticMinimizer
def approximateMaxEigen(H: DenseMatrix[Double]): Double
def approximateMinEigen(H: DenseMatrix[Double]): Double
def computeObjective(h: DenseMatrix[Double], q: DenseVector[Double], x: DenseVector[Double]): Double
def dgetrs(A: DenseMatrix[Double], pivot: Array[Int], x: DenseVector[Double]): Unit

Triangular LU solve for finding y such that y := Ax where A is the LU factorization

Triangular LU solve for finding y such that y := Ax where A is the LU factorization

Value Params
A

vector representation of LU factorization

pivot

pivot from LU factorization

x

the linear term for the solve which will also host the result

def dpotrs(A: DenseMatrix[Double], x: DenseVector[Double]): Unit

Triangular Cholesky solve for finding y through backsolves such that y := Ax

Triangular Cholesky solve for finding y through backsolves such that y := Ax

Value Params
A

vector representation of lower triangular cholesky factorization

x

the linear term for the solve which will also host the result

def gemv(alpha: Double, A: DenseMatrix[Double], x: DenseVector[Double], beta: Double, y: DenseVector[Double]): Unit

y := alpha * A * x + beta * y For DenseMatrix A.

y := alpha * A * x + beta * y For DenseMatrix A.

def main(args: Array[String]): Unit
def normColumn(H: DenseMatrix[Double]): Double
def optimizeWithLBFGS(init: DenseVector[Double], H: DenseMatrix[Double], q: DenseVector[Double]): DenseVector[Double]