Optimization algorithms

The <fitting> block in the atomicrex input file controls the optimization procedure. The tag can be supplemented by two optional attributes as shown in the following example:

<fitting enabled="true" output-interval="100">
...
</fitting>

Here, enabled can be used to selectively activate (True) or deactivate (False) model optimization without the need to remove the entire block. The output-interval attribute sets the number of iterations, between which the code reports its current status (i.e., potential parameters, property data etc.).

At present atomicrex directly supports the following optimization algorithms:

There are several optional parameters, which are provided as attributes within the minimizer tag (see below for examples). They allow one to modify the criteria, which cause the minimization loop to be terminated.

The first parameter conv-threshold defines a condition for the maximum variation of the objective function between successive iterations \(k\) and \(k+1\). The minimization is halted if

\[\frac{\chi^2_k - \chi^2_{k+1}}{\max\left\{\left|\chi^2_k\right|,\left|\chi^2_{k+1}\right|,1\right\}} \leq c_t \, p_m\]

where \(c_t\) is set by conv-threshold and \(p_m\) is the machine precision, which is automatically generated by the code. The default value is \(10^{-5}\).

If the number of iterations exceeds max-iter the optimization is terminated. The default value is 100.

The gradient of the objective function is evaluated numerically using central differences:

\[\left.\frac{d\chi^2}{dx}\right|_{x_0} \approx \frac{\chi^2(x_0+\varepsilon) - \chi^2(x_0-\varepsilon)}{2\varepsilon} + \mathcal{O}(\varepsilon^2)\]

The step width \(\varepsilon\) is specified using the attribute gradient-epsilon and defaults to \(10^{-6}\).

L-BFGS minimizer

atomicrex includes an implementation of the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) minimizer, which is a popular quasi-Newton method, with support for constraints. An extensive description of the algorithm can be found on this Wikipedia page. The minimizer can be activated by inserting the <BFGS> tag in the <fitting> block of the main input file with options provided via attributes as illustrated by the following code block:

<fitting>
     <BFGS conv-threshold="1e-16" max-iter="200" gradient-epsilon="1e-8" />
</fitting>

where the default values for the optional parameters described above have been overriden.

Spa minimizer

The Spa minimizer is actually not a separate optimization algorithm but rather works as a wrapper. It generates randomized initial parameter sets and then invokes another minimization algorithm to optimize the starting parameter set. The following code block illustrates the use of the Spa minimizer. A complete example can be found here:

<fitting>
  <spa max-iter="25" seed="120815">
    <BFGS conv-threshold="1e-10" max-iter="50" />
  </spa>
</fitting>

The max-iter attribute specifies the number of times a new initial parameter set is generated. The seeds attribute allows one to set the seed of the random number generator. Here, the L-BFGS minimizer described above is used. Note that the maximum number of iterations for the latter is set to a rather low value. This procedure is recommended to avoid spending many iterations in the inner loop on incrementally optimizing a parameter set rather than sampling a larger number of different initial parameter sets. If a parameter set has been found, whose objective function is smaller than for any of the previously found sets, the new set is written to file and, depending on the value of the <verbosity> tag, standard output.

Warning

When using the Spa minimizer it is strongly advised to define also bounds for properties such as the lattice parameters. Otherwise the fit can run quickly away and yield unreasonable results. Bounds on the properties are imposed by using the min and max keywords of the respective element.

Warning

Note that optimizers that attempt a “global” minimum search including the Spa minimizer as well as the global algorithms from the NLopt library usually do not minimize individual points very carefully. It is therefore usually strongly advised to optimize the minima from these algorithms further using conventional (local) minimizers with tight convergence criteria.

Optimization via NLopt

atomicrex features an interface to the open-source library NLopt for nonlinear optimization, which provides a large number of algorithms for both global optimization, local derivative-free optimization, and local gradient-based methods. These algorithms can be invoked as illustrated by the following example. A complete example can be found here:

<fitting>
   <nlopt algorithm="LN_NELDERMEAD" stopval="1e-10"/>
</fitting>

The algorithm is selected using the mandatory algorithm attribute, which can assume any of the following string values:

Local gradient based algorithms:

Local, derivative free algorithms:

Global algorithms

Warning

Please note some of the algorithms in NLopt, in particular most of the global-optimization algorithms, do not support unconstrained optimization and will return an error if the user has not supplied finite lower and upper bounds. Bounds for potential parameters are set using the min and max attributes of the elements of the <fit-dof> block as described here.

It is furthermore strongly advised to define also bounds for properties such as the lattice parameters. Otherwise the fit can run quickly away and yield unreasonable results. Bounds on the properties are imposed by using the min and max keywords of the respective element.

The <nlopt> element can be further modified using several optional attributes:

  • seed (int): Set seed for pseudo-random number generator; more information can be found here.

Stopping criteria:

  • stopval (float): Stop when the objective function \(\leq\) stopval.

  • maxeval (int): Stop when the number of function evaluations exceeds maxeval.

  • maxtime (float): Stop when the optimization time (in seconds) exceeds maxtime.

  • ftol_rel (float): Stop when an optimization step (or an estimate of the optimum) changes the objective function value by less than ftol_rel multiplied by the absolute value of the function value.

  • ftol_abs (float): Stop when an optimization step (or an estimate of the optimum) changes the function value by less than ftol_abs.

  • xtol_rel (float): Stop when an optimization step (or an estimate of the optimum) changes every parameter by less than xtol_rel multiplied by the absolute value of the parameter.