annealing

Tries to optimize the maximum multipoint loglikelihood using a dedicated simulated annealing stochastic optimization algorithm.

Synopsis:

The annealing command is invoked either as:

• annealing Options
• annealing LPlateau InitTemp FinalTemp Cooling

Description:

The annealing command tries to improve the loglikelihood of the best known order for the current list of active loci (the best map in the heap) using a dedicated simulated annealing search algorithm. The annealing simulation starts from the temperature InitTemp and repeats until the temperature FinalTemp has been reached using a geometric cooling schedule with a cooling parameter Cooling and constant temperature plateaus of length LPlateau. The initial temperature is automatically reajusted if it is too cold/hot.

During the search, the algorithm gives some feedback by printing the current temperature and a character ``+'' if an imporved map has been found.

Arguments :

• Options : -u to obtain the synopsys of the normal use, -h to print a one line description, -H to print a short help.
• LPlateau: length of constant temperature plateaus in the colling schedule.
• InitTemp FinalTemp: starting/ending temperature for the simulated annealing.
• Colling: cooling schedule parameter. This parameter should be between $0$ and $1$ and is typically taken to be close to $1$. The closest to $1$ the longer but also the better the algorithm.

Returns:

nothing. All the map explored by the algorithm are candidate for the heap.

Example:

   CarthaGene version 1.2-LKH, Copyright (c) 1997-2010 (INRA).

   CarthaGene comes with ABSOLUTELY NO WARRANTY.
   CarthaGene is free software. You are welcome to redistribute it,
   under certain conditions. See the License file for information.

Type 'help' for help.

# we first load a dataset
CG> dsload Data/rh.cg
{1 haploid RH 53 118 /home/tschiex/Dev/carthagene/doc/user/exemple/Data/rh.cg}
# perform linkage group detection (output omitted)
CG> group 0.3 3
...
# select group number 10
CG> mrkselset [groupget 10]

# put a (stupid) map in the heap
CG> sem

Map -1 : log10-likelihood =  -161.87
-------:
 Set : Marker List ...
   1 : G5 G18 G17 G14 G16 G13 G12 G6 G7

# use a (fast) annealing command
CG> annealing 100 100 0.1 0.9

Map -1 : log10-likelihood =  -161.87
-------:
 Set : Marker List ...
   1 : G5 G18 G17 G14 G16 G13 G12 G6 G7

Temp: 100.00 :  +++
Temp: 90.00 :
Temp: 81.00 :  +
Temp: 72.90 :
Temp: 65.61 :
Temp: 59.05 :  +
Temp: 53.14 :
Temp: 47.83 :
Temp: 43.05 :
Temp: 38.74 :
Temp: 34.87 :
Temp: 31.38 :
Temp: 28.24 :
Temp: 25.42 :
Temp: 22.88 :
Temp: 20.59 :
Temp: 18.53 :
Temp: 16.68 :
Temp: 15.01 :
Temp: 13.51 :
Temp: 12.16 :
Temp: 10.94 :
Temp: 9.85 :
Temp: 8.86 :
Temp: 7.98 :
Temp: 7.18 :
Temp: 6.46 :
Temp: 5.81 :
Temp: 5.23 :
Temp: 4.71 :
Temp: 4.24 :
Temp: 3.82 :
Temp: 3.43 :
Temp: 3.09 :
Temp: 2.78 :  +
Temp: 2.50 :
Temp: 2.25 :
Temp: 2.03 :
Temp: 1.82 :
Temp: 1.64 :
Temp: 1.48 :
Temp: 1.33 :
Temp: 1.20 :
Temp: 1.08 :
Temp: 0.97 :
Temp: 0.87 :
Temp: 0.79 :
Temp: 0.71 :
Temp: 0.64 :
Temp: 0.57 :
Temp: 0.52 :
Temp: 0.46 :


# we check the map with a large flip
CG> flips 9 0 0

Single Flip(window size : 9, threshold : 0.00).


Map -1 : log10-likelihood =  -141.81
-------:
 Set : Marker List ...
   1 : G7 G12 G13 G16 G6 G5 G17 G18 G14


 2 2 2 3 2 1 3 3 2  log10
 1 6 7 0 0 9 1 2 8   -141.81
[8 7 6 5 - 3 2 1 0]      0.00


# the map found was indeed optimal

See also:

• greedy (2.6.8)
• flips (2.6.4)
• polish (2.6.5)