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nmplot
Provides direct search optimization algorithms.
SYNOPSIS
newobj = nmplot_new () this = nmplot_destroy (this) this = nmplot_configure (this,key,value) value = nmplot_cget (this,key) this = nmplot_display ( this ) value = nmplot_get ( this , key ) this = nmplot_search ( this ) this = nmplot_restart ( this ) [ this , xdata , ydata , zdata ] = nmplot_contour ( this , xmin , xmax , ymin , ymax , nx , ny ) this = nmplot_historyplot ( this , datafile , mytitle , myxlabel , myylabel ) this = nmplot_simplexhistory ( this , colorforeground , markforeground , markstyle )
Description
This class provides several direct search optimization algorithms based on the simplex method.
The goal of this class is to provide a neldermead component with plotting features. It enables to make fast plots of the algorithm progress through the iterations.
It is a specialized neldermead class, with a specific output command. This output function allows to store the history of several data through the iterations of the algorithm. These data are :
the history of the coordinates of the simplex ,
the history of the function value (averaged on the vertices),
the history of the minimum function value in the simplex,
the history of the size of the simplex (as computed with the sigma+ method).
These data are stored into several data files during the optimization process. Several methods allows to plot the data stored into these data files.
Design
The nmplot component is built on top of the neldermead component. The -outputcommand option (of the neldermead class) is not available since the nmplot class uses its own output function. Additional options -simplexfn, -fbarfn, -foptfn and -sigmafn are provided, which allows to configure the file names where the data is stored.
The nmplot class can be considered as a sample test case of the -outputcommand option of the neldermead class. It gives an example of the situation where the user wants to get specialized outputs out of the neldermead class.
Functions
The following functions are available.
- newobj = nmplot_new ()
Creates a new nmplot object.
- newobj
The new object.
- this = nmplot_destroy (this)
Destroy the given object.
- this
The current object.
- this = nmplot_configure (this,key,value)
Configure the current object with the given value for the given key.
- this
The current object.
- key
a 1-by-1 matrix of strings, the key to configure.
The following keys are available and delegated to the
neldermead
component: -verbose, -verbosetermination, -x0, -maxfunevals, -maxiter, -tolfunabsolute, -tolfunrelative, -tolfunmethod, -tolxabsolute, -tolxrelative, -tolxmethod, -function, -outputcommand, -numberofvariables, -storehistory, -boundsmin, -boundsmax, -nbineqconst, -method, -simplex0method, -coords0, -simplex0length, -simplex0deltausual, -simplex0deltazero, -rho, -chi, -gamma, -sigma, -tolfstdeviationmethod, -tolfstdeviation, -tolsimplexizemethod, -tolsimplexizeabsolute, -tolsimplexizerelative, -tolssizedeltafvmethod, -toldeltafv, -kelleystagnationflag, -kelleynormalizationflag, -kelleystagnationalpha0, -restartflag, -restartdetection, -restartmax, -restarteps, -restartstep, -restartsimplexmethod, -boxnbpoints, -nbineqloops, -ineqscaling. These options are documented in the help page of theneldermead_configure
function.The following specific keys are available.
- -simplexfn
the name of the file containing the history of the simplex (default simplexfn="").
- -fbarfn
the name of the file containing the history of the function value, averaged on the vertices of the simplex (default fbarfn="").
- -foptfn
the name of the file containing the history of the minimum function value in the simplex (default foptfn="").
- -sigmafn
the name of the file containing the history of the size of the simplex (default sigmafn="").
- value
the value.
- value = nmplot_cget (this,key)
Get the value for the given key. If the key is unknown, generates an error.
- this
The current object.
- key
a 1-by-1 matrix of strings, the name of the key to quiery. The list of available keys is the same as for the
nmplot_configure
function.
- value = nmplot_get ( this , key )
Get the value for the given key. If the key is unknown, generates an error.
Most fields are available only after an optimization has been performed with one call to the nmplot_search method.
- this
The current object.
- key
a 1-by-1 matrix of strings, the key to get.
The following keys are available and delegated to the
neldermead
component: -funevals, -iterations, -xopt, -fopt, -historyxopt, -historyfopt, -fx0, -status, -historysimplex, -simplexopt, -restartnb. These options are documented in the help page of theneldermead_configure
function.
- this = nmplot_display ( this )
Display the current settings in the console.
- this
The current object.
- this = nmplot_search ( this )
Performs the optimization associated with the method associated with the -method option and find the optimum.
- this
The current object.
If the -restartflag option is enabled, automatic restarts are performed, based on the -restartdetection option.
- this = nmplot_restart ( this )
Restarts the optimization by updating the simplex and performing a new search.
- this
The current object.
- [ this , xdata , ydata , zdata ] = nmplot_contour ( this , xmin , xmax , ymin , ymax , nx , ny )
Plot the contours of the cost function. The cost function must be a function with two parameters.
- this
The current object.
- xmin , xmax , ymin , ymax
the bounds for the contour plot
- nx , ny
the number of points in the directions x, y
- xdata , ydata , zdata
vectors of data, as required by the contour function
- nmplot_simplexhistory ( this , colorforeground , markforeground , markstyle )
Plots the simplex history on the current graphic window. The colorforeground , markforeground , markstyle options are provided to produce fast plots. Specific settings can still be applied with the usual graphic features.
- this
The current object.
- colorforeground
the color of the foreground for the simplices (default colorforeground=5).
- markforeground
the foreground mark for the simplices (default markforeground = 3).
- markstyle
the mark style for the simplices (default markstyle = 9).
- nmplot_historyplot ( this , datafile , mytitle , myxlabel , myylabel )
Plots the history from the given data file on the current graphic window. The mytitle, myxlabel, myylabel options are provided as a way to produce plots faster. Specific settings can still be applied with the usual graphic features.
- this
The current object.
- datafile
the data file which contains the history. The file is expected to be formatted in a way similar to the files associated with the -fbarfn, -foptfn and -sigmafn files. The default value is the value of the -foptfn option.
- mytitle
the title of the plot. Default value is the empty string.
- myxlabel
the x label for the plot. Default value is the empty string.
- myylabel
the y label for the plot. Default value is the empty string.
- [ this , result ] = nmplot_function ( this , x , index )
Call the cost function and return the value.
- this
The current object.
- x
the point where the function is to be evaluated
- index
optional, a flag to pass to the cost function (default = 1). See the section "The cost function" of the neldermead component for available values of index.
Example
In the following example, we use the fixed shape Spendley et al.
simplex algorithm and find the minimum of a quadratic function.
We begin by defining a quadratic function associated with 2 input variables.
We then define an nmplot object and configure the object so that the "fixed"
shape simplex algorithm is used with the regular initial simplex
associated with the "spendley" key.
Four files are configured, which will contain the history of the simplex,
the history of fbar, fopt and sigma through the iterations.
The search is performed by the nmplot_search
function, which writes the 4 data files during the iterations.
The contour
function is called in order to plot the level sets of
the objective function.
The nmplot_simplexhistory
then uses the history of the simplex, as stored in
the "history.simplex.txt" data file, and plot the various
simplices on the contour plot.
The nmplot_historyplot
function is used with the
files "history.fbar.txt", "history.fopt.txt"
and "history.sigma.txt", which produces 3 plots of the
history of the optimization algorithm through the iterations.
function [y, index]=quadratic(x, index) y = x(1)^2 + x(2)^2 - x(1) * x(2); endfunction function y=quadraticC(x1, x2) [y, index] = quadratic ( [x1,x2], 2 ) endfunction nm = nmplot_new (); nm = nmplot_configure(nm,"-numberofvariables",2); nm = nmplot_configure(nm,"-function",quadratic); nm = nmplot_configure(nm,"-x0",[2.0 2.0]'); nm = nmplot_configure(nm,"-maxiter",100); nm = nmplot_configure(nm,"-maxfunevals",300); nm = nmplot_configure(nm,"-tolxrelative",1.e-8); nm = nmplot_configure(nm,"-simplex0method","spendley"); nm = nmplot_configure(nm,"-method","fixed"); // // Setup output files // nm = nmplot_configure(nm,"-simplexfn","history.simplex.txt"); nm = nmplot_configure(nm,"-fbarfn","history.fbar.txt"); nm = nmplot_configure(nm,"-foptfn","history.fopt.txt"); nm = nmplot_configure(nm,"-sigmafn","history.sigma.txt"); // // Perform optimization // nm = nmplot_search(nm); // Plot the contours of the cost function and the simplex history xmin = -2.0; xmax = 4.0; ymin = -2.0; ymax = 4.0; nx = 100; ny = 100; xdata=linspace(xmin,xmax); ydata=linspace(ymin,ymax); scf(); subplot(2,2,1); contour ( xdata , ydata , quadraticC , [0.1 1.0 2.0 5.0 10.0 15.0 20.0] ) nmplot_simplexhistory ( nm ); subplot(2,2,2); mytitle = "Function Value Average"; myxlabel = "Iterations"; nmplot_historyplot ( nm , "history.fbar.txt" , mytitle , myxlabel ); subplot(2,2,3); mytitle = "Minimum Function Value"; myxlabel = "Iterations"; nmplot_historyplot ( nm , "history.fopt.txt" , mytitle , myxlabel ); subplot(2,2,4); mytitle = "Maximum Oriented length"; myxlabel = "Iterations"; nmplot_historyplot ( nm , "history.sigma.txt" , mytitle, myxlabel ); deletefile("history.simplex.txt"); deletefile("history.fbar.txt"); deletefile("history.fopt.txt"); deletefile("history.sigma.txt"); nm = nmplot_destroy(nm);
See Also
- neldermead — Provides direct search optimization algorithms.
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