Minimizer
Related Doc:
package minima
Perform an exact (e.g., GoldenSectionLS) or inexact (e.g., WolfeLS) line search.
Perform an exact (e.g., GoldenSectionLS) or inexact (e.g., WolfeLS) line search. Search in direction 'dir', returning the distance 'z' to move in that direction.
the current point
the direction to move in
the initial step size
Solve the Non-Linear Programming (NLP) problem by starting at 'x0' and iteratively moving down in the search space to a minimal point.
Solve the Non-Linear Programming (NLP) problem by starting at 'x0' and iteratively moving down in the search space to a minimal point.
the starting point
the initial step size
the tolerance
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.
the coordinate values of the current point
This trait sets the pattern for optimization algorithms for solving Non-Linear Programming (NLP) problems of the form:
minimize f(x) subject to g(x) <= 0 [ optionally g(x) == 0 ]
where f is the objective function to be minimized g is the constraint function to be satisfied, if any
Classes mixing in this trait must implement a function (fg) that rolls the constraints into the objective functions as penalties for constraint violation, a one-dimensional Line Search (LS) algorithm (lineSearch) and an iterative method (solve) that searches for improved solutions (x-vectors with lower objective function values (f(x)).