Note:this routine usesoptional parametersto define choices in the problem specification and in the details of the algorithm. If you wish to use default settings for all of the optional parameters, you need only read Sections 1 to 10 of this document. If, however, you wish to reset some or all of the settings please refer to Section 11 for a detailed description of the algorithm and to Section 12 for a detailed description of the specification of the optional parameters.
e04fff is a forward communication
Derivative-free Optimization (DFO)
solver from the NAG optimization modelling suite (DFLS) for small to medium-scale nonlinear least squares problems with bound constraints.
Here the ${r}_{i}\left(x\right)$ are smooth nonlinear functions called residuals and ${l}_{x}$ and ${u}_{x}$ are $n$-dimensional vectors defining bounds on the variables. Typically, in a calibration or data fitting context, the residuals will be defined as the difference between the data points and a nonlinear model (see Section 2.2.3 in the E04 Chapter Introduction).
e04fff serves as a solver for compatible problems stored as a handle. The handle points to an internal data structure which defines the problem and serves as a means of communication for routines in the NAG optimization modelling suite. To define a compatible problem handle, you must call e04raf followed by e04rmf to initialize it and optionally call e04rhf to define bounds on the variables. If e04rhf is not called, all the variables will be considered free by the solver. It should be noted that e04fff always assumes that the Jacobian of the residuals is dense, therefore, defining a sparse structure for the residuals in the call to e04rmf will have no effect. See Section 3.1 in the E04 Chapter Introduction for more details about the NAG optimization modelling suite.
The solver allows fixing variables with the definition of the bounds. However, the following constraint must be met in order to be able to call the solver:
for all non-fixed variable ${x}_{i}$, the value of ${u}_{x}\left(i\right)-{l}_{x}\left(i\right)$ must be at least twice the starting trust region radius (see the consistency constraint of the optional parameter DFO Starting Trust Region).
The solver is based on a derivative-free trust region framework. This type of method is well suited for small to medium-scale problems (around 100 variables) for which the derivatives are unavailable or not easy to compute, and/or for which the function evaluations are expensive or noisy. For a detailed description of the algorithm see Section 11.
The algorithm behaviour and solver strategy can be modified by various optional parameters (see Section 12) which can be set by e04zmfande04zpf at any time between the initialization of the handle by e04raf and a call to the solver. The optional parameters' names specific for this solver start either with the prefix DFO (Derivative-free Optimization) or DFLS (Derivative-free Least Squares). The default values for these optional parameters are chosen to work well in the general case, but it is recommended you tune them to your particular problem. In particular, if the objective function is known to be noisy, it is highly recommended to set the optional parameter DFO Noisy Problem to $\mathrm{YES}$.
Once the solver has finished, options may be modified for the next solve. The solver may be called repeatedly with various starting points and/or optional parameters.
The underlying algorithm implemented for e04fff is the same as the one used by e04fgf. e04fff serves as a forward communication interface to the derivative-free solver for nonlinear least squares problems.
4References
Cartis C, Fiala J, Marteau B and Roberts L (2018) Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers Technical Report University of Oxford
Cartis C and Roberts L (2017) A Derivative-Free Gauss-Newton Method
Conn A R, Scheinberg K and Vicente L N (2009) Introduction to Derivative-Free Optimization, vol. 8 of MPS-SIAM Series on Optimization MPS/SIAM, Philadelphia
Zhang H, Conn A R and Scheinberg K (2010) A Derivative-Free Algorithm for Least-Squares Minimization SIAM J. Optim.20(6) 3555–3576
5Arguments
1: $\mathbf{handle}$ – Type (c_ptr)Input
On entry: the handle to the problem. It needs to be initialized (e.g., by e04raf) and to hold a problem formulation compatible with e04fff. It must not be changed between calls to the NAG optimization modelling suite.
2: $\mathbf{objfun}$ – Subroutine, supplied by the user.External Procedure
objfun must evaluate the value of the nonlinear residuals ${r}_{i}\left(x\right)$ at a specified point $x$.
On entry: $n$, the current number of decision variables $x$ in the model.
2: $\mathbf{x}\left({\mathbf{nvar}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: $x$, the vector of variable values at which the residuals, ${r}_{i}$, are to be evaluated.
3: $\mathbf{nres}$ – IntegerInput
On entry: ${m}_{r}$, the number of residuals in the problem, as set during the initialization of the handle by e04rmf.
4: $\mathbf{rx}\left({\mathbf{nres}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: the value of the residuals ${r}_{i}\left(x\right)$ at $x$.
5: $\mathbf{inform}$ – IntegerInput/Output
On entry: a non-negative value.
On exit: may be used to indicate that the requested objective value could not be computed. Specifically, it can be set to a negative value:
${\mathbf{inform}}=-1$
The solver will attempt a rescue procedure and request an alternative point. If the rescue procedure fails, the solver will exit with ${\mathbf{ifail}}={\mathbf{17}}$.
${\mathbf{inform}}=-2$
The solver will cleanly exit with ${\mathbf{ifail}}={\mathbf{20}}$ and return the best available point as well as the solve statistics.
7: $\mathbf{ruser}\left(*\right)$ – Real (Kind=nag_wp) arrayUser Workspace
8: $\mathbf{cpuser}$ – Type (c_ptr)User Workspace
objfun is called with the arguments iuser, ruser and cpuser as supplied to e04fff. You should use the arrays iuser and ruser, and the data handle cpuser to supply information to objfun.
objfun must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which e04fff is called. Arguments denoted as Input must not be changed by this procedure.
Note:objfun should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by e04fff. If your code inadvertently does return any NaNs or infinities, e04fff is likely to produce unexpected results.
3: $\mathbf{monit}$ – Subroutine, supplied by the NAG Library or the user.External Procedure
monit is provided to enable you to monitor the progress of the optimization and, if necessary, to halt the optimization process.
If no monitoring is required, monit may be the dummy subroutine e04ffu supplied in the NAG Library.
monit is called at the end of every $i$th step where $i$ is controlled by the optional parameter DFO Monitor Frequency (default value $0$, monit is never called).
On entry: $n$, the current number of decision variables $x$ in the model.
2: $\mathbf{x}\left({\mathbf{nvar}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: the current best point.
3: $\mathbf{inform}$ – IntegerInput/Output
On entry: a non-negative value.
On exit: may be used to request the solver to stop immediately. Specifically, if ${\mathbf{inform}}<0$, then the value of rx will be discarded and the solver will terminate immediately with ${\mathbf{ifail}}={\mathbf{20}}$; otherwise, the solver will proceed normally.
4: $\mathbf{rinfo}\left(100\right)$ – Real (Kind=nag_wp) arrayInput
On entry: best objective value computed and various indicators (the values are as described in the main argument rinfo).
5: $\mathbf{stats}\left(100\right)$ – Real (Kind=nag_wp) arrayInput
On entry: solver statistics at monitoring steps or at the end of the current iteration (the values are as described in the main argument stats).
7: $\mathbf{ruser}\left(*\right)$ – Real (Kind=nag_wp) arrayUser Workspace
8: $\mathbf{cpuser}$ – Type (c_ptr)User Workspace
monit is called with the arguments iuser, ruser and cpuser as supplied to e04fff. You should use the arrays iuser and ruser, and the data handle cpuser to supply information to monit.
monit must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which e04fff is called. Arguments denoted as Input must not be changed by this procedure.
4: $\mathbf{nvar}$ – IntegerInput
On entry: $n$, the current number of decision variables $x$ in the model.
5: $\mathbf{x}\left({\mathbf{nvar}}\right)$ – Real (Kind=nag_wp) arrayInput/Output
On entry: ${x}_{0}$, the initial estimates of the variables, $x$.
On exit: the final values of the variables, $x$.
6: $\mathbf{nres}$ – IntegerInput
On entry: ${m}_{r}$, the number of residuals in the problem. It must be unchanged from the value set during the definition of the objective structure by e04rmf.
Constraint:
${\mathbf{nres}}\ge 0$.
7: $\mathbf{rx}\left({\mathbf{nres}}\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: the values of the residuals at the final point given in x.
8: $\mathbf{rinfo}\left(100\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: optimal objective value and various indicators at monitoring steps or at the end of the final iteration. The measures are given in the table below:
$1$
Objective function value $f\left(x\right)$ (sum of the squared residuals).
$2$
$\rho $, the current lower bound of the trust region.
$3$
$\Delta $, the current size of the trust region.
$4$
The number of interpolation points used by the solver.
$5-100$
Reserved for future use.
9: $\mathbf{stats}\left(100\right)$ – Real (Kind=nag_wp) arrayOutput
On exit: solver statistics at monitoring steps or at the end of the final iteration as given in the table below:
$1$
Number of calls to the objective function.
$2$
Total time spent in the solver (including time spent evaluating the objective).
$3$
Total time spent evaluating the objective function.
11: $\mathbf{ruser}\left(*\right)$ – Real (Kind=nag_wp) arrayUser Workspace
12: $\mathbf{cpuser}$ – Type (c_ptr)User Workspace
iuser, ruser and cpuser are not used by e04fff, but are passed directly to objfun and monit and may be used to pass information to these routines. If you do not need to reference cpuser, it should be initialized to c_null_ptr.
13: $\mathbf{ifail}$ – IntegerInput/Output
On entry: ifail must be set to $0$, $-1$ or $1$ to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of $0$ causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of $-1$ means that an error message is printed while a value of $1$ means that it is not.
If halting is not appropriate, the value $-1$ or $1$ is recommended. If message printing is undesirable, then the value $1$ is recommended. Otherwise, the value $-1$ is recommended since useful values can be provided in some output arguments even when ${\mathbf{ifail}}\ne {\mathbf{0}}$ on exit. When the value $-\mathbf{1}$ or $\mathbf{1}$ is used it is essential to test the value of ifail on exit.
On exit: ${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see Section 6).
6Error Indicators and Warnings
If on entry ${\mathbf{ifail}}=0$ or $-1$, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
Note: in some cases e04fff may return useful information.
${\mathbf{ifail}}=1$
The supplied handle does not define a valid handle to the data structure for the NAG optimization modelling suite. It has not been properly initialized or it has been corrupted.
${\mathbf{ifail}}=2$
The problem is already being solved.
This solver does not support the model defined in the handle.
${\mathbf{ifail}}=4$
On entry, ${\mathbf{nvar}}=\u27e8\mathit{\text{value}}\u27e9$, expected $\mathrm{value}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: nvar must match the current number of variables of the model in the handle.
The information supplied does not match with that previously stored.
On entry, ${\mathbf{nres}}=\u27e8\mathit{\text{value}}\u27e9$ must match that given during the definition of the objective in the handle, i.e., $\u27e8\mathit{\text{value}}\u27e9$.
${\mathbf{ifail}}=5$
Inconsistent optional parameters DFO Trust Region Tolerance${\rho}_{\mathrm{end}}$ and DFO Trust Region Slow Tol${\rho}_{\mathrm{tol}}$.
Constraint: ${\rho}_{\mathrm{end}}<{\rho}_{\mathrm{tol}}$.
Use e04zmf to set compatible option values.
Inconsistent optional parameters DFO Trust Region Tolerance${\rho}_{\mathrm{end}}$ and DFO Starting Trust Region${\rho}_{\mathrm{beg}}$.
Constraint: ${\rho}_{\mathrm{end}}<{\rho}_{\mathrm{beg}}$.
Use e04zmf to set compatible option values.
Optional parameter DFO Starting Trust Region${\rho}_{\mathrm{beg}}=\u27e8\mathit{\text{value}}\u27e9$, ${l}_{x}\left(i\right)=\u27e8\mathit{\text{value}}\u27e9$, ${u}_{x}\left(i\right)=\u27e8\mathit{\text{value}}\u27e9$ and $i=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: if ${l}_{x}\left(i\right)\ne {u}_{x}\left(i\right)$ in coordinate $i$, then ${u}_{x}\left(i\right)-{l}_{x}\left(i\right)\ge 2\times {\rho}_{\mathrm{beg}}$.
Use e04zmf to set compatible option values.
${\mathbf{ifail}}=6$
Initial number of interpolation points $\mathit{ninit}=\u27e8\mathit{\text{value}}\u27e9$, total number of interpolation points $\mathit{npts}=\u27e8\mathit{\text{value}}\u27e9$, number of variables $\mathit{nvar}=\u27e8\mathit{\text{value}}\u27e9$. Constraint: growing interpolation set is only supported for linear models ($\mathit{npts}=\mathit{nvar}+1$). Use DFO Number Interp Points and DFO Number Initial Points to control the number of interpolation points.
There were ${n}_{r}=\u27e8\mathit{\text{value}}\u27e9$ unequal bounds and the optional parameter DFO Number Interp Points$\mathit{npt}=\u27e8\mathit{\text{value}}\u27e9$. Constraint: ${n}_{r}+1\le \mathit{npt}\le \frac{({n}_{r}+1)\times ({n}_{r}+2)}{2}$. Use e04zmf to set compatible option values.
${\mathbf{ifail}}=8$
Maximization is not possible for a nonlinear least squares problem.
${\mathbf{ifail}}=17$
Rescue failed: the trust region could not be reduced further after some function evaluation could not be provided. Check the specification of your objective and whether it needs rescaling. Try a different initial x.
Some initial interpolation points were not provided. Rescue cannot be attempted at this stage. Check the specification of your objective and whether it needs rescaling. Try a different initial x.
${\mathbf{ifail}}=18$
The predicted reduction in a trust region step was non-positive. Check your specification of objfun and whether the function needs rescaling. Try a different initial x.
${\mathbf{ifail}}=19$
A rescue procedure has been called in order to correct damage from rounding errors when computing an update to a quadratic approximation of $F$, but no further progress could be made. Check your specification of objfun and whether the function needs rescaling. Try a different initial x.
${\mathbf{ifail}}=20$
User requested termination after a call to the objective function. inform was set to a value lower than $-1$ within the user-supplied function objfun.
User requested termination during a monitoring step. inform was set to a value lower than $0$ within the user-supplied function monit.
${\mathbf{ifail}}=21$
Maximum number of function evaluations exceeded.
${\mathbf{ifail}}=23$
The solver terminated after the maximum time allowed was exceeded.
Maximum number of seconds exceeded. Use optional parameter Time Limit to reset the limit.
${\mathbf{ifail}}=24$
No progress, the solver was stopped after $\u27e8\mathit{\text{value}}\u27e9$ consecutive slow steps.
Use the optional parameter DFO Maximum Slow Steps to modify the maximum number of slow steps accepted.
The solver stopped after $5\times {\mathbf{DFO\; Maximum\; Slow\; Steps}}$ consecutive slow steps and a trust region above the tolerance set by DFO Trust Region Slow Tol.
${\mathbf{ifail}}=50$
The problem was solved to an acceptable level after $\u27e8\mathit{\text{value}}\u27e9$ consecutive slow iterations.
Use the optional parameter DFO Maximum Slow Steps to modify the maximum number of slow steps accepted.
An unexpected error has been triggered by this routine. Please
contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.
${\mathbf{ifail}}=-399$
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.
7Accuracy
In a non-noisy case, the solver can declare convergence on two conditions.
(i)The trust region radius is below the tolerance ${\rho}_{\mathrm{end}}$ set by the optional parameter DFO Trust Region Tolerance. When this condition is met, the corresponding solution will generally be at a distance smaller than $10\times {\rho}_{\mathrm{end}}$ of a local minimum.
(ii)The sum of the square of the residuals is below the tolerance set by the optional parameter DFLS Small Residuals Tol. In a data fitting context, this condition means that the error between the observed data and the model is smaller than the requested tolerance.
If the objective is declared as noisy by the optional parameter DFO Noisy Problem, the solver declares convergence more conservatively. Instead of stopping with the first condition, the solver will trigger soft restarts (see Section 11 for more details) to ensure it did not get stuck in a flat region because of the noise. The solver then declares convergence when it is reasonably sure that it has reached a local minimum.
(i)The total number of restarts is greater than the limit set by optional parameter DFO Max Soft Restarts and the trust region radius is below the tolerance.
(ii)The number of consecutive restarts that did not manage to decrease the objective function is greater than the limit set by the optional parameter DFO Max Unsucc Soft Restarts.
In addition, this solver can stop if the convergence is deemed too slow on two conditions.
(i)The trust region lower bound is lower than the value set by the optional parameter DFO Trust Region Slow Tol and the number of consecutive slow steps is greater than the value set by DFO Maximum Slow Steps.
(ii)The trust region lower bound is greater than the value set by the optional parameter DFO Trust Region Slow Tol and the number of consecutive slow steps is greater than five times the value set by DFO Maximum Slow Steps.
The slow convergence detection can be deactivated by setting DFO Maximum Slow Steps to $0$.
8Parallelism and Performance
e04fff is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
e04fff makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.
9Further Comments
9.1Description of the Printed Output
The solver can print information to give an overview of the problem and the progress of the computation. The output may be sent to two independent
unit numbers
which are set by optional parameters Print File and Monitoring File. Optional parameters Print Level, Print Options, Monitoring Level and Print Solution determine the exposed level of detail. This allows, for example, a detailed log file to be generated while the condensed information is displayed on the screen.
By default (${\mathbf{Print\; File}}=6$, ${\mathbf{Print\; Level}}=2$), four sections are printed to the standard output: a header, a list of options, an iteration log and a summary.
Header
The header is a message indicating the start of the solver. It should look like:
---------------------------------------------------------------------------
E04F(F|G), Derivative-free solver for nonlinear least squares functions
---------------------------------------------------------------------------
Optional parameters list
If ${\mathbf{Print\; Options}}=\mathrm{YES}$, a list of the optional parameters and their values is printed. The list shows all options of the solver, each displayed on one line. The line contains the option name, its current value and an indicator for how it was set. The options left at their defaults are noted by ‘d’ and the ones you have set are noted by ‘U’. Note that the output format is compatible with the file format expected by e04zpf. The output looks as follows:
Dfo Initial Interp Points = Coordinate * d
Dfo Max Objective Calls = 500 * d
Dfo Max Soft Restarts = 5 * d
Dfo Max Unsucc Soft Restarts = 3 * d
Dfo Maximum Slow Steps = 20 * d
Dfo Noise Level = 0.00000E+00 * d
Problem statistics
If ${\mathbf{Print\; Level}}\ge 2$, statistics on the problem are printed, for example:
Problem Statistics
No of variables 4
free (unconstrained) 2
bounded 2
Objective function LeastSquares
No of residuals 11
Iteration log
If ${\mathbf{Print\; Level}}\ge 2$, the solver will print a summary line for each step. An iteration is considered successful when it yields a decrease of the objective sufficiently close to the decrease predicted by the quadratic model. Each line shows the step number (step), the value of the objective function (obj), the lower bound on the radius of the trust region (rho), and the cumulative number of objective function evaluations (nf). The output looks as follows:
Occasionally, the letter ‘s’ is printed at the end of the line indicating that the progress is considered slow by the slow convergence detection heuristic. After a certain number of consecutive slow steps, the solver is stopped. The limit on the number of slow iterations can be controlled by the optional parameter DFO Maximum Slow Steps and the tolerance on the trust region radius before the solver can be stopped is driven by DFO Trust Region Slow Tol.
If ${\mathbf{Print\; Level}}\ge 3$, each line additionally shows the current value of the trust region radius (delta) as well as the step length (||d||) taken. It might look as follows:
Status: Converged, small residuals
Value of the objective 3.95417E-29
Number of objective function evaluations 15
Number of steps 4
Note that only the iterations that decrease the objective function are printed in the iteration log, meaning that objective evaluations are likely to happen between the last printed iteration and the convergence. This leads to a small difference between the last line of the iteration log and the final summary in terms of the number of function evaluations.
Optionally, if ${\mathbf{Stats\; Time}}=\mathrm{YES}$, the timings are printed:
Timings
Total time spent in the solver 0.056
Time spent in the objective evaluation 0.012
Additionally, if ${\mathbf{Print\; Solution}}=\mathrm{YES}$, the solution is printed along with the bounds:
Internal changes have been made to this routine as follows:
At Mark 27:
Even though the interface of e04fff stays the same, the underlying algorithm underwent a complete overhaul, meaning the results obtained can differ significantly with those of the previous release. The new implementation generally provides better performances as well as:
•additional features to better deal with noisy problems (see Section 11.5)
•an option to start the optimization before the full interpolation set is computed (see Section 11.4).
An option to use the previous implementation instead of the new one has also been added: if DFO Version is set to $26$, the algorithm will behave the same way as in the Mark 26 release. The results may still vary slightly as the internal model optimization algorithm has been improved. When using the Mark 26 implementation, some optional parameter descriptions in the documentation may be slightly inaccurate, please refer to the Mark 26 documentation.
Some optional parameter names have also changed, namely the prefix ‘DFLS’ has been changed to ‘DFO’ for the options that are common between the derivative free least-square solvers (DFLS) and the derivative free solver for general nonlinear objective solvers. The old names still work but are not documented anymore.
The name of the argument ‘mon’ has been updated to monit to be consistent with the rest of the NAG Optimization Suite routines.
For details of all known issues which have been reported for the NAG Library please refer to the Known Issues.
10Example
In this example, we minimize the Kowalik and Osborne function with bounds on some of the variables. In this problem, the number of variables $n=4$ and the number of residuals ${m}_{r}=11$. The residuals ${r}_{i}$ are computed by
This section contains a short description of the algorithm used in e04fff which is based on the collaborative work between NAG and the University of Oxford (Cartis and Roberts (2017) and Cartis et al. (2018)). It uses a model-based derivative-free trust region framework adapted to exploit least squares problems structure.
11.1Derivative-free Trust Region Algorithm
In this section, we are interested in generic problems of the form
where the derivatives of the objective function $f$ are not easily available. A model-based DFO algorithm maintains a set of points ${Y}_{k}$ centred on an iterate ${x}_{k}$ to build quadratic interpolation models of the objective
Note that if the number of interpolation points $\mathit{npt}$ is smaller than $\frac{({n}_{r}+1)\times ({n}_{r}+2)}{2}$, the model chosen is the one for which the Hessian ${H}_{k}$ is the closest to ${H}_{k-1}$ in the Frobenius norm sense.
This model is iteratively optimized over a trust region, updated and moved around the new computed points. More precisely, it can be described as:
DFO Algorithm
1.Initialization
Choose an initial interpolation set ${Y}_{0}$, trust region radius ${\rho}_{\mathrm{beg}}$ and build the first quadratic model ${\varphi}_{0}$.
2.Iterationk
(i)Minimize the model in the trust region to obtain a step ${s}_{k}$.
(ii)If the step is too small, adjust the geometry of the interpolation set and the trust region size ${\rho}_{k}$ and restart the iteration.
(iii)Evaluate the objective at the new point ${x}_{k}+{s}_{k}$.
(iv)Replace a far away point from ${Y}_{k}\times {x}_{k}+{s}_{k}$ to obtain ${Y}_{k+1}$.
(v)If the decrease of the objective is sufficient (successful step), choose ${x}_{k+1}={x}_{k}+{s}_{k}$, else choose ${x}_{k+1}={x}_{k}$.
(vi)Choose ${\rho}_{k+1}$ and adjust the geometry of ${Y}_{k+1}$, if necessary.
(vii)Build ${\varphi}_{k+1}$ using the new interpolation set.
(viii)Stop the algorithm if ${\rho}_{k+1}$is below the chosen tolerance ${\rho}_{\mathrm{end}}$.
In the following sections, we call an iteration ‘successful’ when the trial point ${x}_{k}+{s}_{k}$ is accepted as the next iterate.
11.2Bounds on the Variables
The bounds on the variables are handled during the model optimization step (step 2(i) of DFO Algorithm) with an active set method. If a bound is hit, it is fixed and step 2(i) is restarted.
11.3Adaptation to Nonlinear Least Squares Problems
In the specific case where $f$ is a sum of square $f\left(x\right)={\displaystyle \sum _{i=1}^{{m}_{r}}}{{r}_{i}\left(x\right)}^{2}$, a good approximation of the Hessian of the objective can be
where $J$ is the ${m}_{r}\times n$ first derivative matrix of $f$. This approximation is the main idea behind the Gauss–Newton and Levenberg–Marquardt methods. Following the work of Zhang et al. (2010), it is possible to adapt it to the DFO framework. In e04fff, one linear model is built for each residual ${r}_{i}$
The first expression amounts to making a Gauss–Newton approximation when we are far from a stationary point and the second to a Levenberg–Marquardt approximation when we are close to a stationary point with small residuals.
e04fff integrates this method of building models into the framework presented in the DFO Algorithm.
11.4Growing the Interpolation Set
In the case where the function is very expensive, it might be desirable for the solver to make some progress before the ${n}_{r}+1$ evaluations necessary to build the first interpolation model are done. To get that behaviour, you can set the optional parameter DFO Number Initial Points, controlling the number of initial interpolation points, to a value that is lower than ${n}_{r}+1$. The solver will then start its iteration earlier while adding random perturbations to the interpolation models to ensure that the full space is explored.
It is to be noted that this mode will typically not lead to a faster convergence to the solution and should only be used if early progress is desirable.
11.5Dealing with Noisy Problems
If the problem solved is known to be noisy, declaring it as such to the solver with the optional parameter DFO Noisy Problem will modify the behaviour of the solver to take into account the uncertainty of the function evaluations. The two main features implemented to handle noisy objective functions are:
(i)slow update of the trust regions;
(ii)soft restarts of the algorithm can be performed instead of declaring convergence to ensure the solver did not get stuck in a flat region due to the noise.
A soft restart consists of a reset of the trust region's values to the starting ones and a few objective evaluations to improve the geometry of the interpolation set in the new trust region. It is possible to control the number of objective evaluations performed during a soft restart with the optional parameter DFO Number Soft Restarts Pts. After a set maximum number of restarts (DFO Max Soft Restarts) or maximum number of unsuccessful restarts (DFO Max Unsucc Soft Restarts), the solver will declare convergence in the usual way.
12Optional Parameters
Several optional parameters in e04fff define choices in the problem specification or the algorithm logic. In order to reduce the number of formal arguments of e04fff these optional parameters have associated default values that are appropriate for most problems. Therefore, you need only specify those optional parameters whose values are to be different from their default values.
The remainder of this section can be skipped if you wish to use the default values for all optional parameters.
The optional parameters can be changed by calling e04zmf anytime between the initialization of the handle and the call to the solver. Modification of the optional parameters during intermediate monitoring stops is not allowed. Once the solver finishes, the optional parameters can be altered again for the next solve.
Determines how the initial interpolation points are chosen. If ${\mathbf{DFO\; Initial\; Interp\; Points}}=\mathrm{Coordinate}$, the interpolation points are chosen along the coordinate directions around the initial point. If ${\mathbf{DFO\; Initial\; Interp\; Points}}=\mathrm{Random}$, the initial interpolation points are chosen along random orthogonal directions around the initial point. Set DFO Random Seed to a positive value to fix the random seed and get reproducible results.
Constraint: ${\mathbf{DFO\; Initial\; Interp\; Points}}=\mathrm{Coordinate}$ or $\mathrm{Random}$.
DFO Maximum Slow Steps
$i$
Default $=20$
If ${\mathbf{DFO\; Maximum\; Slow\; Steps}}>0$, this parameter defines the maximum number of consecutive slow iterations ${n}_{\mathrm{slow}}$ allowed. Set ${\mathbf{DFO\; Maximum\; Slow\; Steps}}=0$ to deactivate the slow iteration detection. The algorithm can stop in two situations:
(i)${n}_{\mathrm{slow}}>{\mathbf{DFO\; Maximum\; Slow\; Steps}}$ and $\rho <{\mathbf{DFO\; Trust\; Region\; Slow\; Tol}}$ with ${\mathbf{ifail}}={\mathbf{50}}$,
(ii)${n}_{\mathrm{slow}}>5\times {\mathbf{DFO\; Maximum\; Slow\; Steps}}$ with ${\mathbf{ifail}}={\mathbf{24}}$.
A limit on the number of objective function evaluations the solver is allowed to compute. If the limit is reached, the solver stops with ${\mathbf{ifail}}={\mathbf{21}}$.
The maximum total number of soft restarts that can be performed if the objective function is declared as noisy (${\mathbf{DFO\; Noisy\; Problem}}=\mathrm{YES}$).
The maximum number of consecutive unsuccessful soft restarts that can be performed if the objective function is declared as noisy (${\mathbf{DFO\; Noisy\; Problem}}=\mathrm{YES}$).
Indicates if the function evaluations provided to the solver are noisy. If ${\mathbf{DFO\; Noisy\; Problem}}=\mathrm{YES}$, some algorithmic features will be activated:
(i)The trust region update becomes slower to reflect the decreased confidence in the objective values.
(iii)In addition, if ${\mathbf{DFO\; Noise\; Level}}>0.0$, the solver will trigger a soft restart if all the function values are within the noise level.
DFO Number Initial Points
$i$
Default $=0$
The initial number of interpolation points in ${Y}_{0}$(1) used to build the linear models of the residuals. If ${\mathbf{DFO\; Number\; Initial\; Points}}=0$, the number of points is chosen to be equal to the total number of interpolation points set by DFO Number Interp Points.
If this parameter is chosen to be lower than the maximum set by DFO Number Interp Points, the solver will progressively increase the number of interpolation points until it reaches that value. In this release, it is only possible to grow the interpolation set if DFO Number Interp Points is set to the default value.
If ${\mathbf{DFO\; Number\; Initial\; Points}}<{\mathbf{DFO\; Number\; Interp\; Points}}$, DFO Number Interp Points must be set to the default value.
DFO Number Interp Points
$i$
Default $=0$
The maximum number of interpolation points in ${Y}_{k}$(1) used to build the linear models of the residuals. If ${\mathbf{DFO\; Number\; Interp\; Points}}=0$, the number of points is chosen to be ${n}_{r}+1$ where ${n}_{r}$ is the number of non-fixed variables.
The random seed used to generate the random points used to build the initial model or build the underdetermined models when the interpolation set has not fully grown (${\mathbf{DFO\; Number\; Initial\; Points}}<{\mathbf{DFO\; Number\; Interp\; Points}}$). If ${\mathbf{DFO\; Random\; Seed}}<0$, the random seed will be based on values taken from the real-time clock, potentially resulting in the solver taking a different path each time it is run. Set it to a positive value to get fully reproducible runs.
${\rho}_{\mathrm{beg}}$, the initial trust region radius. This parameter should be set to about one tenth of the greatest expected overall change to a variable: the initial quadratic model will be constructed by taking steps from the initial $x$ of length ${\rho}_{\mathrm{beg}}$ along each coordinate direction. The default value assumes that the variables have an order of magnitude $1$.
${\rho}_{\mathrm{end}}$, the requested trust region radius. The algorithm declares convergence when the trust region radius reaches this limit. It should indicate the absolute accuracy that is required in the final values of the variables.
At Mark 27, the underlying algorithm of e04fff underwent significant changes. This option allows you to continue using the Mark 26 version if it is set to '$\mathrm{26}$'. By default (recommended), the latest version of the code is called.
Constraint: ${\mathbf{DFO\; Version}}=\mathrm{Latest}$ or $\mathrm{26}$.
Infinite Bound Size
$r$
Default $\text{}={10}^{20}$
This defines the ‘infinite’ bound $\mathit{bigbnd}$ in the definition of the problem constraints. Any upper bound greater than or equal to $\mathit{bigbnd}$ will be regarded as $+\infty $ (and similarly any lower bound less than or equal to $-\mathit{bigbnd}$ will be regarded as $-\infty $). Note that a modification of this optional parameter does not influence constraints which have already been defined; only the constraints formulated after the change will be affected.
If $i\ge 0$, the
unit number
for the secondary (monitoring) output. If ${\mathbf{Monitoring\; File}}=-1$, no secondary output is provided. The information output to this unit is controlled by Monitoring Level.
Constraint: ${\mathbf{Monitoring\; File}}\ge -1$.
Monitoring Level
$i$
Default $=4$
This parameter sets the amount of information detail that will be printed by the solver to the secondary output. The meaning of the levels is the same as with Print Level.
If $i\ge 0$, the
unit number
for the primary output of the solver. If ${\mathbf{Print\; File}}=-1$, the primary output is completely turned off independently of other settings. The default value is the advisory message unit number as defined by x04abf at the time of the optional parameters initialization, e.g., at the initialization of the handle. The information output to this unit is controlled by Print Level.
Constraint: ${\mathbf{Print\; File}}\ge -1$.
Print Level
$i$
Default $=2$
This parameter defines how detailed information should be printed by the solver to the primary and secondary output.
$\mathit{i}$
Output
$0$
No output from the solver.
$1$
The Header and Summary.
$2$, $3$, $4$, $5$
Additionally, the Iteration log.
Constraint: $0\le {\mathbf{Print\; Level}}\le 5$.
Print Options
$a$
Default $=\mathrm{YES}$
If ${\mathbf{Print\; Options}}=\mathrm{YES}$, a listing of optional parameters will be printed to the primary output and is always printed to the secondary output.
Constraint: ${\mathbf{Print\; Options}}=\mathrm{YES}$ or $\mathrm{NO}$.
Print Solution
$a$
Default $=\mathrm{NO}$
If ${\mathbf{Print\; Solution}}=\mathrm{YES}$, the solution will be printed to the primary and secondary output.
Constraint: ${\mathbf{Print\; Solution}}=\mathrm{YES}$ or $\mathrm{NO}$.
Stats Time
$a$
Default $=\mathrm{NO}$
This parameter turns on timings of various parts of the algorithm to give a better overview of where most of the time is spent. This might be helpful for a choice of different solving approaches. It is possible to choose between CPU and wall clock time. Choice $\mathrm{YES}$ is equivalent to $\mathrm{WALL\; CLOCK}$.
Constraint: ${\mathbf{Stats\; Time}}=\mathrm{YES}$, $\mathrm{NO}$, $\mathrm{CPU}$ or $\mathrm{WALL\; CLOCK}$.
Time Limit
$r$
Default $\text{}={10}^{6}$
A limit to the number of seconds that the solver can use to solve one problem. If during the convergence check this limit is exceeded, the solver will terminate with ${\mathbf{ifail}}={\mathbf{23}}$.