'\"
'\" Generated from file 'optimize\&.man' by tcllib/doctools with format 'nroff'
'\" Copyright (c) 2004 Arjen Markus <arjenmarkus@users\&.sourceforge\&.net>
'\" Copyright (c) 2004,2005 Kevn B\&. Kenny <kennykb@users\&.sourceforge\&.net>
'\"
.TH "math::optimize" n 1\&.0 tcllib "Tcl Math Library"
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.BS
.SH NAME
math::optimize \- Optimisation routines
.SH SYNOPSIS
package require \fBTcl 8\&.5 9\fR
.sp
package require \fBmath::optimize ?1\&.0?\fR
.sp
\fB::math::optimize::minimum\fR \fIbegin\fR \fIend\fR \fIfunc\fR \fImaxerr\fR
.sp
\fB::math::optimize::maximum\fR \fIbegin\fR \fIend\fR \fIfunc\fR \fImaxerr\fR
.sp
\fB::math::optimize::min_bound_1d\fR \fIfunc\fR \fIbegin\fR \fIend\fR ?\fB-relerror\fR \fIreltol\fR? ?\fB-abserror\fR \fIabstol\fR? ?\fB-maxiter\fR \fImaxiter\fR? ?\fB-trace\fR \fItraceflag\fR?
.sp
\fB::math::optimize::min_unbound_1d\fR \fIfunc\fR \fIbegin\fR \fIend\fR ?\fB-relerror\fR \fIreltol\fR? ?\fB-abserror\fR \fIabstol\fR? ?\fB-maxiter\fR \fImaxiter\fR? ?\fB-trace\fR \fItraceflag\fR?
.sp
\fB::math::optimize::solveLinearProgram\fR \fIobjective\fR \fIconstraints\fR
.sp
\fB::math::optimize::linearProgramMaximum\fR \fIobjective\fR \fIresult\fR
.sp
\fB::math::optimize::nelderMead\fR \fIobjective\fR \fIxVector\fR ?\fB-scale\fR \fIxScaleVector\fR? ?\fB-ftol\fR \fIepsilon\fR? ?\fB-maxiter\fR \fIcount\fR? ??-trace? \fIflag\fR?
.sp
.BE
.SH DESCRIPTION
.PP
This package implements several optimisation algorithms:
.IP \(bu
Minimize or maximize a function over a given interval
.IP \(bu
Solve a linear program (maximize a linear function subject to linear
constraints)
.IP \(bu
Minimize a function of several variables given an initial guess for the
location of the minimum\&.
.PP
.PP
The package is fully implemented in Tcl\&. No particular attention has
been paid to the accuracy of the calculations\&. Instead, the
algorithms have been used in a straightforward manner\&.
.PP
This document describes the procedures and explains their usage\&.
.SH PROCEDURES
.PP
This package defines the following public procedures:
.TP
\fB::math::optimize::minimum\fR \fIbegin\fR \fIend\fR \fIfunc\fR \fImaxerr\fR
Minimize the given (continuous) function by examining the values in the
given interval\&. The procedure determines the values at both ends and in the
centre of the interval and then constructs a new interval of 1/2 length
that includes the minimum\&. No guarantee is made that the \fIglobal\fR
minimum is found\&.
.sp
The procedure returns the "x" value for which the function is minimal\&.
.sp
\fIThis procedure has been deprecated - use min_bound_1d instead\fR
.sp
\fIbegin\fR - Start of the interval
.sp
\fIend\fR - End of the interval
.sp
\fIfunc\fR - Name of the function to be minimized (a procedure taking
one argument)\&.
.sp
\fImaxerr\fR - Maximum relative error (defaults to 1\&.0e-4)
.TP
\fB::math::optimize::maximum\fR \fIbegin\fR \fIend\fR \fIfunc\fR \fImaxerr\fR
Maximize the given (continuous) function by examining the values in the
given interval\&. The procedure determines the values at both ends and in the
centre of the interval and then constructs a new interval of 1/2 length
that includes the maximum\&. No guarantee is made that the \fIglobal\fR
maximum is found\&.
.sp
The procedure returns the "x" value for which the function is maximal\&.
.sp
\fIThis procedure has been deprecated - use max_bound_1d instead\fR
.sp
\fIbegin\fR - Start of the interval
.sp
\fIend\fR - End of the interval
.sp
\fIfunc\fR - Name of the function to be maximized (a procedure taking
one argument)\&.
.sp
\fImaxerr\fR - Maximum relative error (defaults to 1\&.0e-4)
.TP
\fB::math::optimize::min_bound_1d\fR \fIfunc\fR \fIbegin\fR \fIend\fR ?\fB-relerror\fR \fIreltol\fR? ?\fB-abserror\fR \fIabstol\fR? ?\fB-maxiter\fR \fImaxiter\fR? ?\fB-trace\fR \fItraceflag\fR?
Miminizes a function of one variable in the given interval\&.  The procedure
uses Brent's method of parabolic interpolation, protected by golden-section
subdivisions if the interpolation is not converging\&.  No guarantee is made
that a \fIglobal\fR minimum is found\&.  The function to evaluate, \fIfunc\fR,
must be a single Tcl command; it will be evaluated with an abscissa appended
as the last argument\&.
.sp
\fIx1\fR and \fIx2\fR are the two bounds of
the interval in which the minimum is to be found\&.  They need not be in
increasing order\&.
.sp
\fIreltol\fR, if specified, is the desired upper bound
on the relative error of the result; default is 1\&.0e-7\&.  The given value
should never be smaller than the square root of the machine's floating point
precision, or else convergence is not guaranteed\&.  \fIabstol\fR, if specified,
is the desired upper bound on the absolute error of the result; default
is 1\&.0e-10\&.  Caution must be used with small values of \fIabstol\fR to
avoid overflow/underflow conditions; if the minimum is expected to lie
about a small but non-zero abscissa, you consider either shifting the
function or changing its length scale\&.
.sp
\fImaxiter\fR may be used to constrain the number of function evaluations
to be performed; default is 100\&.  If the command evaluates the function
more than \fImaxiter\fR times, it returns an error to the caller\&.
.sp
\fItraceFlag\fR is a Boolean value\&. If true, it causes the command to
print a message on the standard output giving the abscissa and ordinate
at each function evaluation, together with an indication of what type
of interpolation was chosen\&.  Default is 0 (no trace)\&.
.TP
\fB::math::optimize::min_unbound_1d\fR \fIfunc\fR \fIbegin\fR \fIend\fR ?\fB-relerror\fR \fIreltol\fR? ?\fB-abserror\fR \fIabstol\fR? ?\fB-maxiter\fR \fImaxiter\fR? ?\fB-trace\fR \fItraceflag\fR?
Miminizes a function of one variable over the entire real number line\&.
The procedure uses parabolic extrapolation combined with golden-section
dilatation to search for a region where a minimum exists, followed by
Brent's method of parabolic interpolation, protected by golden-section
subdivisions if the interpolation is not converging\&.  No guarantee is made
that a \fIglobal\fR minimum is found\&.  The function to evaluate, \fIfunc\fR,
must be a single Tcl command; it will be evaluated with an abscissa appended
as the last argument\&.
.sp
\fIx1\fR and \fIx2\fR are two initial guesses at where the minimum
may lie\&.  \fIx1\fR is the starting point for the minimization, and
the difference between \fIx2\fR and \fIx1\fR is used as a hint at the
characteristic length scale of the problem\&.
.sp
\fIreltol\fR, if specified, is the desired upper bound
on the relative error of the result; default is 1\&.0e-7\&.  The given value
should never be smaller than the square root of the machine's floating point
precision, or else convergence is not guaranteed\&.  \fIabstol\fR, if specified,
is the desired upper bound on the absolute error of the result; default
is 1\&.0e-10\&.  Caution must be used with small values of \fIabstol\fR to
avoid overflow/underflow conditions; if the minimum is expected to lie
about a small but non-zero abscissa, you consider either shifting the
function or changing its length scale\&.
.sp
\fImaxiter\fR may be used to constrain the number of function evaluations
to be performed; default is 100\&.  If the command evaluates the function
more than \fImaxiter\fR times, it returns an error to the caller\&.
.sp
\fItraceFlag\fR is a Boolean value\&. If true, it causes the command to
print a message on the standard output giving the abscissa and ordinate
at each function evaluation, together with an indication of what type
of interpolation was chosen\&.  Default is 0 (no trace)\&.
.TP
\fB::math::optimize::solveLinearProgram\fR \fIobjective\fR \fIconstraints\fR
Solve a \fIlinear program\fR in standard form using a straightforward
implementation of the Simplex algorithm\&. (In the explanation below: The
linear program has N constraints and M variables)\&.
.sp
The procedure returns a list of M values, the values for which the
objective function is maximal or a single keyword if the linear program
is not feasible or unbounded (either "unfeasible" or "unbounded")
.sp
\fIobjective\fR - The M coefficients of the objective function
.sp
\fIconstraints\fR - Matrix of coefficients plus maximum values that
implement the linear constraints\&. It is expected to be a list of N lists
of M+1 numbers each, M coefficients and the maximum value\&.
.TP
\fB::math::optimize::linearProgramMaximum\fR \fIobjective\fR \fIresult\fR
Convenience function to return the maximum for the solution found by the
solveLinearProgram procedure\&.
.sp
\fIobjective\fR - The M coefficients of the objective function
.sp
\fIresult\fR - The result as returned by solveLinearProgram
.TP
\fB::math::optimize::nelderMead\fR \fIobjective\fR \fIxVector\fR ?\fB-scale\fR \fIxScaleVector\fR? ?\fB-ftol\fR \fIepsilon\fR? ?\fB-maxiter\fR \fIcount\fR? ??-trace? \fIflag\fR?
Minimizes, in unconstrained fashion, a function of several variable over all
of space\&.  The function to evaluate, \fIobjective\fR, must be a single Tcl
command\&. To it will be appended as many elements as appear in the initial guess at
the location of the minimum, passed in as a Tcl list, \fIxVector\fR\&.
.sp
\fIxScaleVector\fR is an initial guess at the problem scale; the first
function evaluations will be made by varying the co-ordinates in \fIxVector\fR
by the amounts in \fIxScaleVector\fR\&.  If \fIxScaleVector\fR is not supplied,
the co-ordinates will be varied by a factor of 1\&.0001 (if the co-ordinate
is non-zero) or by a constant 0\&.0001 (if the co-ordinate is zero)\&.
.sp
\fIepsilon\fR is the desired relative error in the value of the function
evaluated at the minimum\&. The default is 1\&.0e-7, which usually gives three
significant digits of accuracy in the values of the x's\&.
.sp
pp
\fIcount\fR is a limit on the number of trips through the main loop of
the optimizer\&.  The number of function evaluations may be several times
this number\&.  If the optimizer fails to find a minimum to within \fIftol\fR
in \fImaxiter\fR iterations, it returns its current best guess and an
error status\&. Default is to allow 500 iterations\&.
.sp
\fIflag\fR is a flag that, if true, causes a line to be written to the
standard output for each evaluation of the objective function, giving
the arguments presented to the function and the value returned\&. Default is
false\&.
.sp
The \fBnelderMead\fR procedure returns a list of alternating keywords and
values suitable for use with \fBarray set\fR\&. The meaning of the keywords is:
.sp
\fIx\fR is the approximate location of the minimum\&.
.sp
\fIy\fR is the value of the function at \fIx\fR\&.
.sp
\fIyvec\fR is a vector of the best N+1 function values achieved, where
N is the dimension of \fIx\fR
.sp
\fIvertices\fR is a list of vectors giving the function arguments
corresponding to the values in \fIyvec\fR\&.
.sp
\fInIter\fR is the number of iterations required to achieve convergence or
fail\&.
.sp
\fIstatus\fR is 'ok' if the operation succeeded, or 'too-many-iterations'
if the maximum iteration count was exceeded\&.
.sp
\fBnelderMead\fR minimizes the given function using the downhill
simplex method of Nelder and Mead\&.  This method is quite slow - much
faster methods for minimization are known - but has the advantage of being
extremely robust in the face of problems where the minimum lies in
a valley of complex topology\&.
.sp
\fBnelderMead\fR can occasionally find itself "stuck" at a point where
it can make no further progress; it is recommended that the caller
run it at least a second time, passing as the initial guess the
result found by the previous call\&.  The second run is usually very
fast\&.
.sp
\fBnelderMead\fR can be used in some cases for constrained optimization\&.
To do this, add a large value to the objective function if the parameters
are outside the feasible region\&.  To work effectively in this mode,
\fBnelderMead\fR requires that the initial guess be feasible and
usually requires that the feasible region be convex\&.
.PP
.SH NOTES
.PP
Several of the above procedures take the \fInames\fR of procedures as
arguments\&. To avoid problems with the \fIvisibility\fR of these
procedures, the fully-qualified name of these procedures is determined
inside the optimize routines\&. For the user this has only one
consequence: the named procedure must be visible in the calling
procedure\&. For instance:
.CS


    namespace eval ::mySpace {
       namespace export calcfunc
       proc calcfunc { x } { return $x }
    }
    #
    # Use a fully-qualified name
    #
    namespace eval ::myCalc {
       puts [min_bound_1d ::myCalc::calcfunc $begin $end]
    }
    #
    # Import the name
    #
    namespace eval ::myCalc {
       namespace import ::mySpace::calcfunc
       puts [min_bound_1d calcfunc $begin $end]
    }

.CE
The simple procedures \fIminimum\fR and \fImaximum\fR have been
deprecated: the alternatives are much more flexible, robust and
require less function evaluations\&.
.SH EXAMPLES
.PP
Let us take a few simple examples:
.PP
Determine the maximum of f(x) = x^3 exp(-3x), on the interval (0,10):
.CS


proc efunc { x } { expr {$x*$x*$x * exp(-3\&.0*$x)} }
puts "Maximum at: [::math::optimize::max_bound_1d efunc 0\&.0 10\&.0]"

.CE
.PP
The maximum allowed error determines the number of steps taken (with
each step in the iteration the interval is reduced with a factor 1/2)\&.
Hence, a maximum error of 0\&.0001 is achieved in approximately 14 steps\&.
.PP
An example of a \fIlinear program\fR is:
.PP
Optimise the expression 3x+2y, where:
.CS


   x >= 0 and y >= 0 (implicit constraints, part of the
                     definition of linear programs)

   x + y   <= 1      (constraints specific to the problem)
   2x + 5y <= 10

.CE
.PP
This problem can be solved as follows:
.CS



   set solution [::math::optimize::solveLinearProgram  { 3\&.0   2\&.0 }  { { 1\&.0   1\&.0   1\&.0 }
        { 2\&.0   5\&.0  10\&.0 } } ]

.CE
.PP
Note, that a constraint like:
.CS


   x + y >= 1

.CE
can be turned into standard form using:
.CS


   -x  -y <= -1

.CE
.PP
The theory of linear programming is the subject of many a text book and
the Simplex algorithm that is implemented here is the best-known
method to solve this type of problems, but it is not the only one\&.
.SH "BUGS, IDEAS, FEEDBACK"
This document, and the package it describes, will undoubtedly contain
bugs and other problems\&.
Please report such in the category \fImath :: optimize\fR of the
\fITcllib Trackers\fR [http://core\&.tcl\&.tk/tcllib/reportlist]\&.
Please also report any ideas for enhancements you may have for either
package and/or documentation\&.
.PP
When proposing code changes, please provide \fIunified diffs\fR,
i\&.e the output of \fBdiff -u\fR\&.
.PP
Note further that \fIattachments\fR are strongly preferred over
inlined patches\&. Attachments can be made by going to the \fBEdit\fR
form of the ticket immediately after its creation, and then using the
left-most button in the secondary navigation bar\&.
.SH KEYWORDS
linear program, math, maximum, minimum, optimization
.SH CATEGORY
Mathematics
.SH COPYRIGHT
.nf
Copyright (c) 2004 Arjen Markus <arjenmarkus@users\&.sourceforge\&.net>
Copyright (c) 2004,2005 Kevn B\&. Kenny <kennykb@users\&.sourceforge\&.net>

.fi
