NonlinearMinimizer

Type Members

type BDV = DenseVector[Double]
case class Projection(proximal: Proximal) extends Product with Serializable
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)

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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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
final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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def main(args: Array[String]): Unit
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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

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def toString(): String

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final def wait(): Unit

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

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@throws( ... )
final def wait(arg0: Long): Unit

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Related Docs: class NonlinearMinimizer | package proximal

object NonlinearMinimizer extends Serializable

Type Members

type BDV = DenseVector[Double]

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

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

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

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

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

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

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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