Author: bugman
Date: Wed Aug 27 13:42:43 2014
New Revision: 25336
URL: http://svn.gna.org/viewcvs/relax?rev=25336&view=rev
Log:
Changed the optimisation description in the relaxation curve-fitting chapter of
the manual.
The script example has been converted to match the sample script, replacing the
Nelder-Mead simplex
algorithm with Newton optimisation, and removing the argument turning diagonal
scaling off. All the
text about only the simplex algorithm being supported due to the missing
gradients and Hessians in
the C module have been deleted. The text that linear constraints are not
supported has also been
removed - but this was fixed when the logarithmic barrier constraint algorithm
was added to minfx.
Modified:
trunk/docs/latex/curvefit.tex
Modified: trunk/docs/latex/curvefit.tex
URL:
http://svn.gna.org/viewcvs/relax/trunk/docs/latex/curvefit.tex?rev=25336&r1=25335&r2=25336&view=diff
==============================================================================
--- trunk/docs/latex/curvefit.tex (original)
+++ trunk/docs/latex/curvefit.tex Wed Aug 27 13:42:43 2014
@@ -268,13 +268,13 @@
minimise.grid_search(inc=11)
# Minimise.
-minimise.execute('simplex', scaling=False, constraints=False)
+minimise.execute('newton', constraints=False)
# Monte Carlo simulations.
monte_carlo.setup(number=500)
monte_carlo.create_data()
monte_carlo.initial_values()
-minimise.execute('simplex', scaling=False, constraints=False)
+minimise.execute('newton', constraints=False)
monte_carlo.error_analysis()
# Save the relaxation rates.
@@ -505,17 +505,11 @@
minimise.grid_search(inc=11)
\end{lstlisting}
-The next step is to select one of the minimisation algorithms to optimise the
model parameters.
-Currently for relaxation curve-fitting only simplex minimisation is supported.
-This is because the relaxation curve-fitting C module is incomplete only
implementing the chi-squared function.
-The chi-squared gradient (the vector of first partial derivatives) and
chi-squared Hessian (the matrix of second partial derivatives) are not yet
implemented in the C modules and hence optimisation algorithms which only
employ function calls are supported.
-Simplex minimisation is the only technique in relax which fits this criterion.
-In addition constraints cannot be used as the constraint algorithm is
dependent on gradient calls.
-Therefore the minimisation command for relaxation curve-fitting is forced to be
+The next step is to select one of the minimisation algorithms to optimise the
model parameters
\begin{lstlisting}[firstnumber=65]
# Minimise.
-minimise.execute('simplex', constraints=False)
+minimise.execute('newton', constraints=False)
\end{lstlisting}
@@ -550,7 +544,7 @@
Then exactly the same optimisation as was used for the model can be performed
\begin{lstlisting}[firstnumber=72]
-minimise.execute('simplex', constraints=False)
+minimise.execute('newton', constraints=False)
\end{lstlisting}
The parameter errors are then determined as the standard deviation of the
optimised parameter values of the simulations
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