class NNLS extends SerializableLogging

NNLS solves nonnegative least squares problems using a modified projected gradient method.

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Instance Constructors

  1. new NNLS(maxIters: Int = -1)

    maxIters

    user defined maximum iterations

Type Members

  1. type BDM = DenseMatrix[Double]
  2. type BDV = DenseVector[Double]
  3. case class State extends Product with Serializable

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  5. def clone(): AnyRef
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  6. final def eq(arg0: AnyRef): Boolean
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  8. final def getClass(): Class[_]
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  9. def hashCode(): Int
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  10. def initialize(n: Int): State

    Solve a least squares problem, possibly with nonnegativity constraints, by a modified projected gradient method.

    Solve a least squares problem, possibly with nonnegativity constraints, by a modified projected gradient method. That is, find x minimising ||Ax - b||_2 given AT A and AT b.

    We solve the problem min_x 1/2 x' ata x' - x'atb subject to x >= 0

    The method used is similar to one described by Polyak (B. T. Polyak, The conjugate gradient method in extremal problems, Zh. Vychisl. Mat. Mat. Fiz. 9(4)(1969), pp. 94-112) for bound- constrained nonlinear programming. Polyak unconditionally uses a conjugate gradient direction, however, while this method only uses a conjugate gradient direction if the last iteration did not cause a previously-inactive constraint to become active.

  11. final def isInstanceOf[T0]: Boolean
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  12. def logger: LazyLogger
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  13. val maxIters: Int
  14. def minimize(ata: DenseMatrix[Double], atb: DenseVector[Double], init: State): DenseVector[Double]
  15. def minimize(ata: DenseMatrix[Double], atb: DenseVector[Double]): DenseVector[Double]
  16. def minimizeAndReturnState(ata: DenseMatrix[Double], atb: DenseVector[Double]): State
  17. def minimizeAndReturnState(ata: DenseMatrix[Double], atb: DenseVector[Double], initialState: State, resetState: Boolean = true): State

    minimizeAndReturnState allows users to hot start the solver using initialState.

    minimizeAndReturnState allows users to hot start the solver using initialState. If a initialState is provided and resetState is set to false, the optimizer will hot start using the previous state. By default resetState is true and every time reset will be called on the incoming state

    ata

    gram matrix

    atb

    linear term

    initialState

    initial state for calling the solver from inner loops

    resetState

    reset the state based on the flag

    returns

    converged state

  18. final def ne(arg0: AnyRef): Boolean
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  19. final def notify(): Unit
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  20. final def notifyAll(): Unit
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  21. def reset(ata: DenseMatrix[Double], atb: DenseVector[Double], state: State): State
  22. final def synchronized[T0](arg0: ⇒ T0): T0
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  23. def toString(): String
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  24. final def wait(arg0: Long, arg1: Int): Unit
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  25. final def wait(arg0: Long): Unit
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  26. final def wait(): Unit
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  1. def finalize(): Unit
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    Deprecated

    (Since version ) see corresponding Javadoc for more information.

Inherited from SerializableLogging

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