[comment {-*- tcl -*- doctools manpage}]
[manpage_begin simulation::annealing n 0.3]
[keywords math]
[keywords optimization]
[keywords {simulated annealing}]
[copyright {2008 Arjen Markus <arjenmarkus@users.sourceforge.net>}]
[moddesc   {Tcl Simulation Tools}]
[titledesc {Simulated annealing}]
[category  Mathematics]
[require Tcl [opt "8.5 9"]]
[require simulation::annealing 0.3]

[description]
[para]
The technique of [emph "simulated annealing"] provides methods to
estimate the global optimum of a function. It is described in some
detail on the Wiki [uri http://wiki.tcl.tk/...]. The idea is simple:

[list_begin itemized]
[item]
randomly select points within a given search space
[item]
evaluate the function to be optimised for each of these
points and select the point that has the lowest (or highest)
function value or - sometimes - accept a point that has a less optimal
value. The chance by which such a non-optimal point is accepted diminishes over
time.
[item]
Accepting less optimal points means the method does not necessarily get
stuck in a local optimum and theoretically it is capable of finding the
global optimum within the search space.
[list_end]

The method resembles the cooling of material, hence the name.
[para]

The package [term simulation::annealing] offers the command [term findMinimum]:

[example {
    puts [::simulation::annealing::findMinimum \
        -trials 300 \
        -parameters {x -5.0 5.0 y -5.0 5.0} \
        -function {$x*$x+$y*$y+sin(10.0*$x)+4.0*cos(20.0*$y)}]
}]

prints the estimated minimum value of the function f(x,y) =
[emph {x**2+y**2+sin(10*x)+4*cos(20*y)}] and the values of x and y where
the minimum was attained:

[example {
result -4.9112922923 x -0.181647676593 y 0.155743646974
}]

[section "PROCEDURES"]

The package defines the following auxiliary procedures:

[list_begin definitions]

[call [cmd ::simulation::annealing::getOption] [arg keyword]]

Get the value of an option given as part of the [term findMinimum]
command.

[list_begin arguments]
[arg_def string keyword] Given keyword (without leading minus)
[list_end]

[para]

[call [cmd ::simulation::annealing::hasOption] [arg keyword]]

Returns 1 if the option is available, 0 if not.

[list_begin arguments]
[arg_def string keyword] Given keyword (without leading minus)
[list_end]

[para]

[call [cmd ::simulation::annealing::setOption] [arg keyword] [arg value]]

Set the value of the given option.

[list_begin arguments]
[arg_def string keyword] Given keyword (without leading minus)
[arg_def string value] (New) value for the option
[list_end]

[list_end]

The main procedures are [term findMinimum] and [term findCombinatorialMinimum]:

[list_begin definitions]

[call [cmd ::simulation::annealing::findMinimum] [arg args]]

Find the minimum of a function using simulated annealing. The function
and the method's parameters is given via a list of
keyword-value pairs.

[list_begin args]
[arg_def int n] List of keyword-value pairs, all of which are available
during the execution via the [term getOption] command.
[list_end]

[call [cmd ::simulation::annealing::findCombinatorialMinimum] [arg args]]

Find the minimum of a function of discrete variables using simulated
annealing. The function and the method's parameters is given via a list of
keyword-value pairs.

[list_begin args]
[arg_def int n] List of keyword-value pairs, all of which are available
during the execution via the [term getOption] command.
[list_end]

[list_end]

The [term findMinimum] command predefines the following options:

[list_begin itemized]
[item]
[term "-parameters list"]: triples defining parameters and ranges
[item]
[term "-function expr"]: expression defining the function
[item]
[term "-code body"]: body of code to define the function (takes
precedence over [term -function]). The code should set the variable
"result"
[item]
[term "-init code"]: code to be run at start up
[term "-final code"]: code to be run at the end
[term "-trials n"]: number of trials before reducing the temperature
[term "-reduce factor"]: reduce the temperature by this factor (between 0 and 1)
[term "-initial-temp t"]: initial temperature
[term "-scale s"]: scale of the function (order of magnitude of the values)
[term "-estimate-scale y/n"]: estimate the scale (only if [term -scale]
is not present)
[term "-verbose y/n"]: print detailed information on progress to the
report file (1) or not (0)
[term "-reportfile file"]: opened file to print to (defaults to stdout)
[list_end]
Any other options can be used via the getOption procedure
in the body.

The [term findCombinatorialMinimum] command predefines the following
options:

[list_begin itemized]
[item]
[term "-number-params n"]: number of binary parameters (the solution
space consists of lists of 1s and 0s). This is a required option.
[item]
[term "-initial-values"]: list of 1s and 0s constituting the start of
the search.
[list_end]
The other predefined options are identical to those of [term findMinimum].

[section TIPS]
The procedure [term findMinimum] works by constructing a temporary
procedure that does the actual work. It loops until the point
representing the estimated optimum does not change anymore within the
given number of trials. As the temperature gets lower and lower the
chance of accepting a point with a higher value becomes lower too, so
the procedure will in practice terminate.

[para]
It is possible to optimise over a non-rectangular region, but some care
must be taken:
[list_begin itemized]
[item]
If the point is outside the region of interest, you can specify a very
high value.
[item]
This does mean that the automatic determination of a scale factor is
out of the question - the high function values that force the point
inside the region would distort the estimation.
[list_end]

Here is an example of finding an optimum inside a circle:

[example {
    puts [::simulation::annealing::findMinimum \
        -trials 3000 \
        -reduce 0.98 \
        -parameters {x -5.0 5.0 y -5.0 5.0} \
        -code {
            if { hypot($x-5.0,$y-5.0) < 4.0 } {
                set result [expr {$x*$x+$y*$y+sin(10.0*$x)+4.0*cos(20.0*$y)}]
            } else {
                set result 1.0e100
            }
        }]
}]

The method is theoretically capable of determining the global optimum,
but often you need to use a large number of trials and a slow reduction
of temperature to get reliable and repeatable estimates.

[para]
You can use the [term -final] option to use a deterministic optimization
method, once you are sure you are near the required optimum.

[para]
The [term findCombinatorialMinimum] procedure is suited for situations
where the parameters have the values 0 or 1 (and there can be many of
them). Here is an example:

[list_begin itemized]

[item]
We have a function that attains an absolute minimum if the first ten
numbers are 1 and the rest is 0:

[example {
proc cost {params} {
    set cost 0
    foreach p [lrange $params 0 9] {
        if { $p == 0 } {
            incr cost
        }
    }
    foreach p [lrange $params 10 end] {
        if { $p == 1 } {
            incr cost
        }
    }
    return $cost
}
}]

[item]
We want to find the solution that gives this minimum for various lengths
of the solution vector [term params]:

[example {
foreach n {100 1000 10000} {
    break
    puts "Problem size: $n"
    puts [::simulation::annealing::findCombinatorialMinimum \
        -trials 300 \
        -verbose 0 \
        -number-params $n \
        -code {set result [cost $params]}]
}
}]

[item]
As the vector grows, the computation time increases, but the procedure
will stop if some kind of equilibrium is reached. To achieve a useful
solution you may want to try different values of the trials parameter
for instance. Also ensure that the function to be minimized depends on
all or most parameters - see the source code for a counter example and
run that.
[list_end]
[manpage_end]
