SG++-Doxygen-Documentation
python.leja Namespace Reference

## Functions

def calc_min (f, lower_bound, upper_bound)

def invert_maximum_leja (next_point, z, lower_bound, upper_bound, weight=lambda x :1)

def leja_points (start, count, lower_bound, upper_bound, weight=lambda x :1, debug=False, view=False)

def leja_poly (next_point, z, lower_bound, upper_bound, weight=lambda x :1)

def maximum_leja (next_point, z, lower_bound, upper_bound, weight=lambda x :1)

## Variables

int count = 8

int lower_bound = 0

def points = leja_points(start, count, lower_bound, upper_bound, weight)

float start = 0.5

int upper_bound = 1

weight = lambda x : np.sin(x * np.pi)

## ◆ calc_min()

 def python.leja.calc_min ( f, lower_bound, upper_bound )

Referenced by python.leja.leja_points().

## ◆ invert_maximum_leja()

 def python.leja.invert_maximum_leja ( next_point, z, lower_bound, upper_bound, weight = lambda x : 1 )
The Nelder-Mead Simplex algorithm used to find the next leja point searches
for the minimum of the function, so we just invert our maximum function


References python.leja.maximum_leja().

Referenced by python.leja.leja_points().

## ◆ leja_points()

 def python.leja.leja_points ( start, count, lower_bound, upper_bound, weight = lambda x : 1, debug = False, view = False )
calculates the next COUNT leja points with START = z_0
returns the leja points in a list


## ◆ leja_poly()

 def python.leja.leja_poly ( next_point, z, lower_bound, upper_bound, weight = lambda x : 1 )

References python.leja.weight.

## ◆ maximum_leja()

 def python.leja.maximum_leja ( next_point, z, lower_bound, upper_bound, weight = lambda x : 1 )
the maximums function for the leja points
the next leja point is the input so that this function returns
its maximum


References python.leja.weight.

Referenced by python.leja.invert_maximum_leja().

## ◆ lower_bound

 int python.leja.lower_bound = 0

## ◆ upper_bound

 int python.leja.upper_bound = 1