SpectralProjectedGradient

Instance Constructors

new SpectralProjectedGradient(projection: (T) ⇒ T = ((t: T) => t), tolerance: Double = 1.0E-6, suffDec: Double = 1.0E-4, minImprovementWindow: Int = 10, alphaMax: Double = 1.0E10, alphaMin: Double = 1.0E-10, maxIter: Int = 500, testOpt: Boolean = true, initFeas: Boolean = false, maxSrchIt: Int = 30)(implicit space: MutableVectorField[T, Double])

projection
projection operations
alphaMax
longest step
alphaMin
shortest step
testOpt
perform optimality check based on projected gradient at each iteration
initFeas
is the initial guess feasible, or should it be projected?
maxSrchIt
maximum number of line search attempts

Type Members

type History = Double

Any history the derived minimization function needs to do its updates.
Any history the derived minimization function needs to do its updates. typically an approximation to the second derivative/hessian matrix.

Definition Classes
SpectralProjectedGradient → FirstOrderMinimizer
class SimpleLineSearch extends ApproximateLineSearch
case class State(x: T, value: Double, grad: T, adjustedValue: Double, adjustedGradient: T, iter: Int, initialAdjVal: Double, history: History, fVals: IndexedSeq[Double] = ..., numImprovementFailures: Int = 0, searchFailed: Boolean = false) extends Product with Serializable

Tracks the information about the optimizer, including the current point, its value, gradient, and then any history.

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def !=(arg0: Any): Boolean

Definition Classes
Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def ==(arg0: Any): Boolean

Definition Classes
Any
def adjust(newX: T, newGrad: T, newVal: Double): (Double, T)

Attributes
protected
Definition Classes
SpectralProjectedGradient → FirstOrderMinimizer
def adjustFunction(f: DF): DF

Attributes
protected
Definition Classes
FirstOrderMinimizer
val alphaMax: Double

longest step
val alphaMin: Double

shortest step
final def asInstanceOf[T0]: T0

Definition Classes
Any
def calculateObjective(f: DF, x: T, history: History): (Double, T)

Attributes
protected
Definition Classes
FirstOrderMinimizer
def chooseDescentDirection(state: State, f: DF): T

Attributes
protected
Definition Classes
SpectralProjectedGradient → FirstOrderMinimizer
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def determineStepSize(state: State, f: DF, direction: T): Double

Attributes
protected
Definition Classes
SpectralProjectedGradient → FirstOrderMinimizer
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
def infiniteIterations(f: DF, init: T): Iterator[State]

Definition Classes
FirstOrderMinimizer
val initFeas: Boolean

is the initial guess feasible, or should it be projected?
def initialHistory(f: DF, init: T): History

Attributes
protected
Definition Classes
SpectralProjectedGradient → FirstOrderMinimizer
def initialState(f: DF, init: T): State

Attributes
protected
Definition Classes
FirstOrderMinimizer
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def iterations(f: DF, init: T): Iterator[State]

Definition Classes
FirstOrderMinimizer
def logger: LazyLogger

Attributes
protected
Definition Classes
SerializableLogging
val maxSrchIt: Int

maximum number of line search attempts
def minimize(f: DF, init: T): T

Definition Classes
FirstOrderMinimizer → Minimizer
def minimizeAndReturnState(f: DF, init: T): State

Definition Classes
FirstOrderMinimizer
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numberOfImprovementFailures: Int

Definition Classes
FirstOrderMinimizer
def projectedVector(x: T, g: T)(implicit vspace: Module[T, Double]): T

Definition Classes
Projecting
val projection: (T) ⇒ T

projection operations
projection operations

Definition Classes
SpectralProjectedGradient → Projecting
val suffDec: Double
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def takeStep(state: State, dir: T, stepSize: Double): T

Attributes
protected
Definition Classes
SpectralProjectedGradient → FirstOrderMinimizer
val testOpt: Boolean

perform optimality check based on projected gradient at each iteration
def toString(): String

Definition Classes
AnyRef → Any
def updateFValWindow(oldState: State, newAdjVal: Double): IndexedSeq[Double]

Attributes
protected
Definition Classes
FirstOrderMinimizer
def updateHistory(newX: T, newGrad: T, newVal: Double, f: DF, oldState: State): History

Attributes
protected
Definition Classes
SpectralProjectedGradient → FirstOrderMinimizer
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

class SpectralProjectedGradient[T, DF <: DiffFunction[T]] extends FirstOrderMinimizer[T, DF] with Projecting[T] with SerializableLogging

Instance Constructors

Type Members

type History = Double

class SimpleLineSearch extends ApproximateLineSearch

case class State(x: T, value: Double, grad: T, adjustedValue: Double, adjustedGradient: T, iter: Int, initialAdjVal: Double, history: History, fVals: IndexedSeq[Double] = ..., numImprovementFailures: Int = 0, searchFailed: Boolean = false) extends Product with Serializable

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def adjust(newX: T, newGrad: T, newVal: Double): (Double, T)

def adjustFunction(f: DF): DF

val alphaMax: Double

val alphaMin: Double

final def asInstanceOf[T0]: T0

def calculateObjective(f: DF, x: T, history: History): (Double, T)

def chooseDescentDirection(state: State, f: DF): T

def clone(): AnyRef

def determineStepSize(state: State, f: DF, direction: T): Double

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def infiniteIterations(f: DF, init: T): Iterator[State]

val initFeas: Boolean

def initialHistory(f: DF, init: T): History

def initialState(f: DF, init: T): State

final def isInstanceOf[T0]: Boolean

def iterations(f: DF, init: T): Iterator[State]

def logger: LazyLogger

val maxSrchIt: Int

def minimize(f: DF, init: T): T

def minimizeAndReturnState(f: DF, init: T): State

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numberOfImprovementFailures: Int

def projectedVector(x: T, g: T)(implicit vspace: Module[T, Double]): T

val projection: (T) ⇒ T

val suffDec: Double

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

def takeStep(state: State, dir: T, stepSize: Double): T

val testOpt: Boolean

def toString(): String

def updateFValWindow(oldState: State, newAdjVal: Double): IndexedSeq[Double]

def updateHistory(newX: T, newGrad: T, newVal: Double, f: DF, oldState: State): History

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Projecting[T]

Inherited from FirstOrderMinimizer[T, DF]

Inherited from SerializableLogging

Inherited from Serializable

Inherited from Serializable

Inherited from Minimizer[T, DF]

Inherited from AnyRef

Inherited from Any

Ungrouped