class L_BFGS_B extends Minimizer

The L_BFGS_B the class implements the Limited memory Broyden–Fletcher– Goldfarb–Shanno for Bound constrained optimization (L-BFGS-B) Quasi-Newton Algorithm for solving Non-Linear Programming (NLP) problems. L-BFGS-B determines a search direction by deflecting the steepest descent direction vector (opposite the gradient) by * multiplying it by a matrix that approximates the inverse Hessian. Furthermore, only a few vectors represent the approximation of the Hessian Matrix (limited memory). The parameters estimated are also bounded within user specified lower and upper bounds.

minimize f(x) subject to g(x) <= 0 [ optionally g(x) == 0 ]

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Instance Constructors

  1. new L_BFGS_B(f: FunctionV2S, g: FunctionV2S = null, ineq: Boolean = true, exactLS: Boolean = false, l: VectoD = null, u: VectoD = null)

    f

    the objective function to be minimized

    g

    the constraint function to be satisfied, if any

    ineq

    whether the constraint is treated as inequality (default) or equality

    exactLS

    whether to use exact (e.g., GoldenLS) or inexact (e.g., WolfeLS) Line Search

    l

    vector of lower bounds for all input parameters

    u

    vector of upper bounds for all input parameters

Type Members

  1. type Pair = (VectorD, VectorD)
    Definition Classes
    Minimizer

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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  4. val EPSILON: Double
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    protected
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    Minimizer
  5. val MAX_ITER: Int
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    protected
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    Minimizer
  6. val STEP: Double
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    protected
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    Minimizer
  7. val TOL: Double
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    protected
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    Minimizer
  8. final def asInstanceOf[T0]: T0
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  9. def clone(): AnyRef
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    protected[java.lang]
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    @native() @throws( ... )
  10. final def eq(arg0: AnyRef): Boolean
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  11. def equals(arg0: Any): Boolean
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  12. def fg(x: VectorD): Double

    The objective function f plus a weighted penalty based on the constraint function g.

    The objective function f plus a weighted penalty based on the constraint function g.

    x

    the coordinate values of the current point

    Definition Classes
    L_BFGS_B → Minimizer
  13. def finalize(): Unit
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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]
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    @native()
  15. def hashCode(): Int
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    @native()
  16. final def isInstanceOf[T0]: Boolean
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  17. def lineSearch(x: VectorD, dir: VectorD, step: Double = STEP): Double

    Perform an exact 'GoldenSectionLS' or inexact 'WolfeLS' Line Search.

    Perform an exact 'GoldenSectionLS' or inexact 'WolfeLS' Line Search. Search in direction 'dir', returning the distance 'z' to move in that direction.

    x

    the current point

    dir

    the direction to move in

    step

    the initial step size

    Definition Classes
    L_BFGS_B → Minimizer
  18. final def ne(arg0: AnyRef): Boolean
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  19. final def notify(): Unit
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    @native()
  20. final def notifyAll(): Unit
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    @native()
  21. def setHistorySize(hs_: Int): Unit

    Modify the number of historical vectors to store.

    Modify the number of historical vectors to store.

    hs_

    the new history size

  22. def solve(x0: VectorD, alphaInit: Double = STEP, toler: Double = TOL): VectorD

    Solve the following Non-Linear Programming (NLP) problem using L-BFGS-B: min { f(x) | g(x) <= 0 }.

    Solve the following Non-Linear Programming (NLP) problem using L-BFGS-B: min { f(x) | g(x) <= 0 }.

    x0

    the starting point

    alphaInit

    the initial step size

    toler

    the tolerance

    Definition Classes
    L_BFGS_B → Minimizer
  23. final def synchronized[T0](arg0: ⇒ T0): T0
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  24. def toString(): String
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  25. final def wait(): Unit
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    @throws( ... )
  26. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  27. final def wait(arg0: Long): Unit
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