class CoordinateDescent extends Minimizer with Error
The CoordinateDescent class solves unconstrained Non-Linear Programming (NLP)
problems using the Coordinate Descent algorithm. Given a function 'f' and a
starting point 'x0', the algorithm picks coordinate directions (cyclically) and
takes steps in the those directions. The algorithm iterates until it converges.
dir_k = kth coordinate direction
minimize f(x)
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new
CoordinateDescent(f: FunctionV2S, exactLS: Boolean = true)
- f
the vector-to-scalar objective function
- exactLS
whether to use exact (e.g.,
GoldenLS) or inexact (e.g.,WolfeLS) Line Search
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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'.
The objective function 'f' plus a weighted penalty based on the constraint function 'g'. Override for constrained optimization and ignore for unconstrained optimization.
- x
the coordinate values of the current point
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finalize(): Unit
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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
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def
solve(x0: VectorD, step: Double = STEP, toler: Double = EPSILON): VectorD
Solve the Non-Linear Programming (NLP) problem using the Coordinate Descent algorithm.
Solve the Non-Linear Programming (NLP) problem using the Coordinate Descent algorithm.
- x0
the starting point
- step
the initial step size
- toler
the tolerance
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