scitbx.direct_search_simulated_annealing
index
/net/chevy/raid1/nat/src/cctbx_project/scitbx/direct_search_simulated_annealing.py

 
Modules
       
scitbx.array_family.flex
math
random
scitbx.simplex

 
Classes
       
__builtin__.object
dssa
test_rosenbrock_function

 
class dssa(__builtin__.object)
    Directed Simplex Simulated Annealing
http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/go_files/DSSA.pdf
Hybrid simulated annealing and direct search method for nonlinear unconstrained global optimization
 
  Methods defined here:
FindCentroidPt(self, kk)
FindReflectionPt(self, kk)
ReplacePt(self, kk, reflect_matrix, reflect_value)
__init__(self, dimension, matrix, evaluator, further_opt=False, n_candidate=None, tolerance=1e-08, max_iter=1000000000.0, coolfactor=0.6, T_ratio=10000.0, simplex_scale=0.2, monitor_cycle=11)
explore(self)
function(self, point)
get_candi(self)
get_solution(self)
initialize(self, matrix)
optimize(self)
optimize_further(self)
sort(self)
update_candi(self)

Data descriptors defined here:
__dict__
dictionary for instance variables (if defined)
__weakref__
list of weak references to the object (if defined)

 
class test_rosenbrock_function(__builtin__.object)
     Methods defined here:
__init__(self, dim=4)
target(self, vector)

Data descriptors defined here:
__dict__
dictionary for instance variables (if defined)
__weakref__
list of weak references to the object (if defined)

 
Functions
       
run()

 
Data
        division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)