class SpectralProjectedGradient[T] extends FirstOrderMinimizer[T, DiffFunction[T]] with Projecting[T] with SerializableLogging
SPG is a Spectral Projected Gradient minimizer; it minimizes a differentiable function subject to the optimum being in some set, given by the projection operator projection
- T
vector type
Linear Supertypes
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Inherited
- SpectralProjectedGradient
- Projecting
- FirstOrderMinimizer
- SerializableLogging
- Serializable
- Serializable
- Minimizer
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Instance Constructors
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new
SpectralProjectedGradient(projection: (T) ⇒ T = (t: T) =>
t, tolerance: Double = 1e-6, suffDec: Double = 1e-4, fvalMemory: Int = 30, alphaMax: Double = 1e10, alphaMin: Double = 1e-10, bbMemory: Int = 10, maxIter: Int = -1, initFeas: Boolean = false, curvilinear: Boolean = false, bbType: Int = 1, maxSrcht: Int = 30)(implicit space: MutableVectorField[T, Double])- projection
projection operations
- tolerance
termination criterion: tolerance for norm of projected gradient
- suffDec
sufficient decrease parameter
- alphaMax
longest step
- alphaMin
shortest step
- bbMemory
number of history entries for linesearch
- maxIter
maximum number of iterations
- initFeas
is the initial guess feasible, or should it be projected?
- curvilinear
if curvilinear true, do the projection inside line search in place of doing it in chooseDescentDirection
- maxSrcht
maximum number of iterations inside line search
Type Members
- case class History(alphaBB: Double, fvals: IndexedSeq[Double]) extends Product with Serializable
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type
State = FirstOrderMinimizer.State[T, Info, History]
- Definition Classes
- FirstOrderMinimizer
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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final
def
##(): Int
- Definition Classes
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final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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def
adjust(newX: T, newGrad: T, newVal: Double): (Double, T)
- Attributes
- protected
- Definition Classes
- FirstOrderMinimizer
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def
adjustFunction(f: DiffFunction[T]): DiffFunction[T]
- Attributes
- protected
- Definition Classes
- FirstOrderMinimizer
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final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
bbAlpha(s: T, y: T): Double
From Mark Schmidt's Matlab code if bbType == 1 alpha = (s'*s)/(s'*y); else alpha = (s'*y)/(y'*y);
From Mark Schmidt's Matlab code if bbType == 1 alpha = (s'*s)/(s'*y); else alpha = (s'*y)/(y'*y);
- Attributes
- protected
- val bbType: Int
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def
calculateObjective(f: DiffFunction[T], x: T, history: History): (Double, T)
- Attributes
- protected
- Definition Classes
- FirstOrderMinimizer
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def
chooseDescentDirection(state: State, f: DiffFunction[T]): T
- Attributes
- protected
- Definition Classes
- SpectralProjectedGradient → FirstOrderMinimizer
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
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- @native() @HotSpotIntrinsicCandidate() @throws( ... )
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val
convergenceCheck: ConvergenceCheck[T]
- Definition Classes
- FirstOrderMinimizer
- val curvilinear: Boolean
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def
determineStepSize(state: State, f: DiffFunction[T], direction: T): Double
- Attributes
- protected
- Definition Classes
- SpectralProjectedGradient → FirstOrderMinimizer
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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final
def
getClass(): Class[_]
- Definition Classes
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- @native() @HotSpotIntrinsicCandidate()
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def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
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def
infiniteIterations(f: DiffFunction[T], state: State): Iterator[State]
- Definition Classes
- FirstOrderMinimizer
- val initFeas: Boolean
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def
initialHistory(f: DiffFunction[T], init: T): History
- Attributes
- protected
- Definition Classes
- SpectralProjectedGradient → FirstOrderMinimizer
-
def
initialState(f: DiffFunction[T], init: T): State
- Attributes
- protected
- Definition Classes
- FirstOrderMinimizer
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
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def
iterations(f: DiffFunction[T], init: T): Iterator[State]
- Definition Classes
- FirstOrderMinimizer
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def
logger: LazyLogger
- Attributes
- protected
- Definition Classes
- SerializableLogging
- val maxSrcht: Int
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def
minimize(f: DiffFunction[T], init: T): T
- Definition Classes
- FirstOrderMinimizer → Minimizer
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def
minimizeAndReturnState(f: DiffFunction[T], init: T): State
- Definition Classes
- FirstOrderMinimizer
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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final
def
notify(): Unit
- Definition Classes
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- @native() @HotSpotIntrinsicCandidate()
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final
def
notifyAll(): Unit
- Definition Classes
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- Annotations
- @native() @HotSpotIntrinsicCandidate()
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def
projectedVector(x: T, g: T)(implicit vspace: Module[T, Double]): T
- Definition Classes
- Projecting
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val
projection: (T) ⇒ T
- Definition Classes
- SpectralProjectedGradient → Projecting
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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
-
def
toString(): String
- Definition Classes
- AnyRef → Any
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def
updateHistory(newX: T, newGrad: T, newVal: Double, f: DiffFunction[T], oldState: State): History
- Attributes
- protected
- Definition Classes
- SpectralProjectedGradient → FirstOrderMinimizer
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final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
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- @throws( ... )
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final
def
wait(arg0: Long): Unit
- Definition Classes
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final
def
wait(): Unit
- Definition Classes
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- @throws( ... )