breeze.optimize.proximal

NonlinearMinimizer

object NonlinearMinimizer extends Serializable

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  1. type BDV = DenseVector[Double]

  2. case class Projection(proximal: Proximal) extends Product with Serializable

  3. case class ProximalPrimal[T](primal: DiffFunction[T], u: T, z: T, rho: Double)(implicit space: MutableInnerProductModule[T, Double]) extends DiffFunction[T] with Product with Serializable

    Proximal modifications to Primal algorithm for scaled ADMM formulation AdmmObj(x, u, z) = f(x) + rho/2*||x - z + u||2 dAdmmObj/dx = df/dx + rho*(x - z + u)

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  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  6. def apply(ndim: Int, constraint: Constraint, lambda: Double, usePQN: Boolean = false): FirstOrderMinimizer[BDV, DiffFunction[BDV]]

    A compansion object to generate projection based minimizer that can use SPG/PQN as the solver

    A compansion object to generate projection based minimizer that can use SPG/PQN as the solver

    ndim

    the problem dimension

    constraint

    one of the available constraint, possibilities are x>=0; lb<=x<=ub;aeq*x = beq; 1'x = s, x >= 0; ||x||1 <= s

    lambda

    the regularization parameter for most of the constraints

    returns

    FirstOrderMinimizer to optimize on f(x) and proximal operator

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

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  9. final def eq(arg0: AnyRef): Boolean

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  15. def main(args: Array[String]): Unit

  16. final def ne(arg0: AnyRef): Boolean

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def project(proximal: Proximal, maxIter: Int = 1, m: Int = 10, tolerance: Double = 1e-6, usePQN: Boolean = false): FirstOrderMinimizer[BDV, DiffFunction[BDV]]

    A subset of proximal operators can be represented as Projection operators and for those operators, we give an option to the user to choose a projection based algorithm.

    A subset of proximal operators can be represented as Projection operators and for those operators, we give an option to the user to choose a projection based algorithm. The options available for users are SPG (Spectral Projected Gradient) and PQN (Projected Quasi Newton)

    proximal

    operator that defines proximal algorithm

    returns

    FirstOrderMinimizer to optimize on f(x) and proximal operator

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

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