'\"
'\" Generated from file 'annealing\&.man' by tcllib/doctools with format 'nroff'
'\" Copyright (c) 2008 Arjen Markus <arjenmarkus@users\&.sourceforge\&.net>
'\"
.TH "simulation::annealing" n 0\&.3 tcllib "Tcl Simulation Tools"
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.SH NAME
simulation::annealing \- Simulated annealing
.SH SYNOPSIS
package require \fBTcl ?8\&.5 9?\fR
.sp
package require \fBsimulation::annealing 0\&.3\fR
.sp
\fB::simulation::annealing::getOption\fR \fIkeyword\fR
.sp
\fB::simulation::annealing::hasOption\fR \fIkeyword\fR
.sp
\fB::simulation::annealing::setOption\fR \fIkeyword\fR \fIvalue\fR
.sp
\fB::simulation::annealing::findMinimum\fR \fIargs\fR
.sp
\fB::simulation::annealing::findCombinatorialMinimum\fR \fIargs\fR
.sp
.BE
.SH DESCRIPTION
.PP
The technique of \fIsimulated annealing\fR provides methods to
estimate the global optimum of a function\&. It is described in some
detail on the Wiki \fIhttp://wiki\&.tcl\&.tk/\&.\&.\&.\fR\&. The idea is simple:
.IP \(bu
randomly select points within a given search space
.IP \(bu
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\&.
.IP \(bu
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\&.
.PP
The method resembles the cooling of material, hence the name\&.
.PP
The package \fIsimulation::annealing\fR offers the command \fIfindMinimum\fR:
.CS


    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)}]

.CE
prints the estimated minimum value of the function f(x,y) =
\fIx**2+y**2+sin(10*x)+4*cos(20*y)\fR and the values of x and y where
the minimum was attained:
.CS


result -4\&.9112922923 x -0\&.181647676593 y 0\&.155743646974

.CE
.SH PROCEDURES
The package defines the following auxiliary procedures:
.TP
\fB::simulation::annealing::getOption\fR \fIkeyword\fR
Get the value of an option given as part of the \fIfindMinimum\fR
command\&.
.RS
.TP
string \fIkeyword\fR
Given keyword (without leading minus)
.RE
.sp
.TP
\fB::simulation::annealing::hasOption\fR \fIkeyword\fR
Returns 1 if the option is available, 0 if not\&.
.RS
.TP
string \fIkeyword\fR
Given keyword (without leading minus)
.RE
.sp
.TP
\fB::simulation::annealing::setOption\fR \fIkeyword\fR \fIvalue\fR
Set the value of the given option\&.
.RS
.TP
string \fIkeyword\fR
Given keyword (without leading minus)
.TP
string \fIvalue\fR
(New) value for the option
.RE
.PP
The main procedures are \fIfindMinimum\fR and \fIfindCombinatorialMinimum\fR:
.TP
\fB::simulation::annealing::findMinimum\fR \fIargs\fR
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\&.
.RS
.TP
int \fIn\fR
List of keyword-value pairs, all of which are available
during the execution via the \fIgetOption\fR command\&.
.RE
.TP
\fB::simulation::annealing::findCombinatorialMinimum\fR \fIargs\fR
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\&.
.RS
.TP
int \fIn\fR
List of keyword-value pairs, all of which are available
during the execution via the \fIgetOption\fR command\&.
.RE
.PP
The \fIfindMinimum\fR command predefines the following options:
.IP \(bu
\fI-parameters list\fR: triples defining parameters and ranges
.IP \(bu
\fI-function expr\fR: expression defining the function
.IP \(bu
\fI-code body\fR: body of code to define the function (takes
precedence over \fI-function\fR)\&. The code should set the variable
"result"
.IP \(bu
\fI-init code\fR: code to be run at start up
\fI-final code\fR: code to be run at the end
\fI-trials n\fR: number of trials before reducing the temperature
\fI-reduce factor\fR: reduce the temperature by this factor (between 0 and 1)
\fI-initial-temp t\fR: initial temperature
\fI-scale s\fR: scale of the function (order of magnitude of the values)
\fI-estimate-scale y/n\fR: estimate the scale (only if \fI-scale\fR
is not present)
\fI-verbose y/n\fR: print detailed information on progress to the
report file (1) or not (0)
\fI-reportfile file\fR: opened file to print to (defaults to stdout)
.PP
Any other options can be used via the getOption procedure
in the body\&.
The \fIfindCombinatorialMinimum\fR command predefines the following
options:
.IP \(bu
\fI-number-params n\fR: number of binary parameters (the solution
space consists of lists of 1s and 0s)\&. This is a required option\&.
.IP \(bu
\fI-initial-values\fR: list of 1s and 0s constituting the start of
the search\&.
.PP
The other predefined options are identical to those of \fIfindMinimum\fR\&.
.SH TIPS
The procedure \fIfindMinimum\fR 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\&.
.PP
It is possible to optimise over a non-rectangular region, but some care
must be taken:
.IP \(bu
If the point is outside the region of interest, you can specify a very
high value\&.
.IP \(bu
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\&.
.PP
Here is an example of finding an optimum inside a circle:
.CS


    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
            }
        }]

.CE
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\&.
.PP
You can use the \fI-final\fR option to use a deterministic optimization
method, once you are sure you are near the required optimum\&.
.PP
The \fIfindCombinatorialMinimum\fR 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:
.IP \(bu
We have a function that attains an absolute minimum if the first ten
numbers are 1 and the rest is 0:
.CS


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
}

.CE
.IP \(bu
We want to find the solution that gives this minimum for various lengths
of the solution vector \fIparams\fR:
.CS


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]}]
}

.CE
.IP \(bu
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\&.
.PP
.SH KEYWORDS
math, optimization, simulated annealing
.SH CATEGORY
Mathematics
.SH COPYRIGHT
.nf
Copyright (c) 2008 Arjen Markus <arjenmarkus@users\&.sourceforge\&.net>

.fi
