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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- 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
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- val EPSILON: Double
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- val MAX_ITER: Int
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- val STEP: Double
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- val TOL: Double
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- def fg(x: VectorD): Double
The objective function f plus a weighted penalty based on the constraint function g.
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- def lineSearch(x: VectorD, dir: VectorD, step: Double = STEP): Double
Perform an exact 'GoldenSectionLS' or inexact 'WolfeLS' Line Search.
- final def ne(arg0: AnyRef): Boolean
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- 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
- 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 }.
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