trait Minimizer extends AnyRef
The Minimizer 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)).
- Alphabetic
- By Inheritance
- Minimizer
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Abstract Value Members
-
abstract
def
lineSearch(x: VectorD, dir: VectorD, step: Double = STEP): Double
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.
- x
the current point
- dir
the direction to move in
- step
the initial step size
-
abstract
def
solve(x0: VectorD, step: Double = STEP, toler: Double = EPSILON): VectorD
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.
- x0
the starting point
- step
the initial step size
- toler
the tolerance
Concrete Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
val
EPSILON: Double
- Attributes
- protected
-
val
MAX_ITER: Int
- Attributes
- protected
-
val
STEP: Double
- Attributes
- protected
-
val
TOL: Double
- Attributes
- protected
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
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
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )