ConjugateGradient

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def cgIteration(A: CSCMatrix[Double], b: DenseVector[Double], state: CGState): CGState

a single Fletcher-Reeves Conjugate Gradient iteration (matrix A should not change between iterations)

def cgIterator(A: CSCMatrix[Double], b: DenseVector[Double], initial: CGState): Iterator[CGState]

create an iterator of Conjugate Gradient steps

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

final def getClass(): Class[_]

def hashCode(): Int

def initialState(A: CSCMatrix[Double], b: DenseVector[Double], x: DenseVector[Double]): CGState

create an initial CG step (use for cgIterator)

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def solveSparse(A: CSCMatrix[Double], b: DenseVector[Double], xInit: DenseVector[Double], tolerance: Double, maxIter: Int): DenseVector[Double]

solve system A*x = b, for a sparse matrix A using the Conjugate Gradient algorithm

def solveSparse(A: CSCMatrix[Double], b: DenseVector[Double], tolerance: Double = 1e-10): DenseVector[Double]

solve system A*x = b, for a sparse matrix A using the Conjugate Gradient algorithm uses initial guess of zero

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

def finalize(): Unit