Standard Output
Dakota prints information on algorithm progress, function evaluations, and summary results to standard out and error (screen, terminal, or console) as it runs. Detailed information for a function evaluation may include an evaluation number, parameter values, execution syntax, the active set vector, and the response data set.
This output may optionally be redirected to a file using command-line options
or the keyword output_file.
Note
Whenever an output_file is specified for a Dakota run, the screen output itself becomes quite minimal, consisting of version statements, environment statements and execution times.
Simple Output Example
Output from a Rosenbrock multidimensional parameter study (see Listing 24) is shown here:
{Writing new restart file dakota.rst
methodName = multidim_parameter_study
gradientType = none
hessianType = none
>>>>> Running multidim_parameter_study iterator.
Multidimensional parameter study for variable partitions of
8
8
------------------------------
Begin Function Evaluation 1
------------------------------
Parameters for function evaluation 1:
-2.0000000000e+00 x1
-2.0000000000e+00 x2
Direct function: invoking rosenbrock
Active response data for function evaluation 1:
Active set vector = { 1 }
3.6090000000e+03 response_fn_1
.
.
.
<<<<< Function evaluation summary: 81 total (81 new, 0 duplicate)
Simple Correlation Matrix among all inputs and outputs:
x1 x2 response_fn_1
x1 1.00000e+00
x2 1.73472e-17 1.00000e+00
response_fn_1 -3.00705e-03 -5.01176e-01 1.00000e+00
.
.
.
<<<<< Iterator multidim_parameter_study completed.}
Excluding the copy of the input file at the beginning and timing information at the end, the file is organized into three basic parts:
1. Information on the problem For this example, we see that a new restart file is being created and Dakota has carried out a multi- dim parameter study with 8 partitions for each of two variables. 2. Information on each function evaluation Each function evaluation is numbered. Details for function evaluation 1 show that at input variable values x1 = −2.0 and x2 = −2.0, the direct rosenbrock function is being evaluated. There is one response with a value of 3.609e+03. 3. Summary statistics The function evaluation summary is preceded by <<<<<. For this example 81 total evaluations were assessed; all were new, none were read in from the restart file. Correlation matrices complete the statistics and output for this problem. Successful runs will finish with <<<<< Iterator study type completed.
Detailed Output
Here, optimization of the “container” problem is used as an example to describe Dakota output. The input file shown in Listing 25 specifies one equality constraint, and that Dakota’s finite difference algorithm will provide central difference numerical gradients to the NPSOL optimizer.
dakota/share/dakota/examples/users/container_opt_npsol.in# Dakota Input File: container_opt_npsol.in
environment
tabular_data
tabular_data_file = 'container_opt_npsol.dat'
method
## (NPSOL requires a software license; if not available, try
## conmin_mfd or optpp_q_newton instead)
npsol_sqp
variables
continuous_design = 2
descriptors 'H' 'D'
initial_point 4.5 4.5
lower_bounds 0.0 0.0
interface
analysis_drivers = 'container'
fork
parameters_file = 'container.in'
results_file = 'container.out'
file_tag
responses
objective_functions = 1
nonlinear_equality_constraints = 1
numerical_gradients
method_source dakota
interval_type central
fd_step_size = 0.001
no_hessians
A partial listing of the Dakota output for the container optimization example follows:
Dakota version 5.4+ (stable) released May 2 2014.
Subversion revision 2508 built May 2 2014 15:26:14.
Running MPI Dakota executable in serial mode.
Start time: Fri May 2 15:38:11 2014
----------------------------------------------------------------
Begin DAKOTA input file
/home/user/dakota/build/examples/users/container_opt_npsol.in
----------------------------------------------------------------
# Dakota Input File: container_opt_npsol.in
environment
<SNIP>
---------------------
End DAKOTA input file
---------------------
Using Dakota input file '/home/user/dakota/build/examples/users/container_opt_npsol.in'
Writing new restart file dakota.rst
>>>>> Executing environment.
>>>>> Running npsol_sqp iterator.
------------------------------------------
Begin Dakota derivative estimation routine
------------------------------------------
>>>>> Initial map for analytic portion of response:
---------------------
Begin Evaluation 1
---------------------
Parameters for evaluation 1:
4.5000000000e+00 H
4.5000000000e+00 D
blocking fork: container container.in.1 container.out.1
Active response data for evaluation 1:
Active set vector = { 1 1 }
1.0713145108e+02 obj_fn
8.0444076396e+00 nln_eq_con_1
>>>>> Dakota finite difference gradient evaluation for x[1] + h:
---------------------
Begin Evaluation 2
---------------------
Parameters for evaluation 2:
4.5045000000e+00 H
4.5000000000e+00 D
blocking fork: container container.in.2 container.out.2
Active response data for evaluation 2:
Active set vector = { 1 1 }
1.0719761302e+02 obj_fn
8.1159770472e+00 nln_eq_con_1
>>>>> Dakota finite difference gradient evaluation for x[1] - h:
---------------------
Begin Evaluation 3
---------------------
Parameters for evaluation 3:
4.4955000000e+00 H
4.5000000000e+00 D
blocking fork: container container.in.3 container.out.3
Active response data for evaluation 3:
Active set vector = { 1 1 }
1.0706528914e+02 obj_fn
7.9728382320e+00 nln_eq_con_1
>>>>> Dakota finite difference gradient evaluation for x[2] + h:
---------------------
Begin Evaluation 4
---------------------
Parameters for evaluation 4:
4.5000000000e+00 H
4.5045000000e+00 D
blocking fork: container container.in.4 container.out.4
Active response data for evaluation 4:
Active set vector = { 1 1 }
1.0727959301e+02 obj_fn
8.1876180243e+00 nln_eq_con_1
>>>>> Dakota finite difference gradient evaluation for x[2] - h:
---------------------
Begin Evaluation 5
---------------------
Parameters for evaluation 5:
4.5000000000e+00 H
4.4955000000e+00 D
blocking fork: container container.in.5 container.out.5
Active response data for evaluation 5:
Active set vector = { 1 1 }
1.0698339109e+02 obj_fn
7.9013403937e+00 nln_eq_con_1
>>>>> Total response returned to iterator:
Active set vector = { 3 3 } Deriv vars vector = { 1 2 }
1.0713145108e+02 obj_fn
8.0444076396e+00 nln_eq_con_1
[ 1.4702653619e+01 3.2911324639e+01 ] obj_fn gradient
[ 1.5904312809e+01 3.1808625618e+01 ] nln_eq_con_1 gradient
<SNIP>
>>>>> Dakota finite difference gradient evaluation for x[2] - h:
---------------------
Begin Evaluation 40
---------------------
Parameters for evaluation 40:
4.9873894231e+00 H
4.0230575428e+00 D
blocking fork: container container.in.40 container.out.40
Active response data for evaluation 40:
Active set vector = { 1 1 }
9.8301287596e+01 obj_fn
-1.2698647501e-01 nln_eq_con_1
>>>>> Total response returned to iterator:
Active set vector = { 3 3 } Deriv vars vector = { 1 2 }
9.8432498116e+01 obj_fn
-9.6918029158e-12 nln_eq_con_1
[ 1.3157517860e+01 3.2590159623e+01 ] obj_fn gradient
[ 1.2737124497e+01 3.1548877601e+01 ] nln_eq_con_1 gradient
NPSOL exits with INFORM code = 0 (see "Interpretation of output" section in NPSOL manual)
NOTE: see Fortran device 9 file (fort.9 or ftn09)
for complete NPSOL iteration history.
<<<<< Function evaluation summary: 40 total (40 new, 0 duplicate)
<<<<< Best parameters =
4.9873894231e+00 H
4.0270846274e+00 D
<<<<< Best objective function =
9.8432498116e+01
<<<<< Best constraint values =
-9.6918029158e-12
<<<<< Best evaluation ID: 36
<<<<< Iterator npsol_sqp completed.
<<<<< Environment execution completed.
DAKOTA execution time in seconds:
Total CPU = 0.03 [parent = 0.023997, child = 0.006003]
Total wall clock = 0.090703
The output begins with information on the Dakota version, compilation
date, and run mode. It then echos the user input file before proceeding
to execution phase. The lines following
>>>>> Running npsol_sqp iterator. show Dakota performing function
evaluations 1–5 that have been requested by NPSOL. Evaluations 6 through
39 have been omitted from the listing for brevity.
Immediately following the line Begin Function Evaluation 1, the
initial values of the design variables, the syntax of the blocking fork
function evaluation, and the resulting objective and constraint function
values returned by the simulation are listed. The values of the design
variables are labeled with the tags H and D, respectively,
according to the descriptors given in the input file,
Listing 25. The values of the objective
function and volume constraint are labeled with the tags obj_fn and
nln_eq_con_1, respectively. Note that the initial design parameters
are infeasible since the equality constraint is violated
(\(\ne 0\)). However, by the end of the run, the optimizer finds a
design that is both feasible and optimal for this example. Between the
design variables and response values, the content of the system call to
the simulator is displayed as
(container container.in.1 container.out.1), with container
being the name of the simulator and container.in.1 and
container.out.1 being the names of the parameters and results
files, respectively.
Just preceding the output of the objective and constraint function
values is the line Active set vector = {1 1}. The active set
vector indicates the types of data that are required from the simulator
for the objective and constraint functions, and values of “1”
indicate that the simulator must return values for these functions
(gradient and Hessian data are not required).
Note
Refer to the Active Set Vector section for more details.
Since finite difference gradients have been specified, Dakota computes
their values by making additional function evaluation requests to the
simulator at perturbed parameter values. Examples of the
gradient-related function evaluations have been included in the sample
output, beginning with the line that reads
>>>>> Dakota finite difference evaluation for x[1] + h:. The
resulting finite difference gradients are listed after function
evaluation 5 beginning with the line
>>>>> Total response returned to iterator:. Here, another active
set vector is displayed in the Dakota output file. The line
Active set vector = { 3 3 } indicates that the total response
resulting from the finite differencing contains function values and
gradients.
The final lines of the Dakota output, beginning with the line
<<<<< Function evaluation summary:, summarize the results of the
optimization study. The best values of the optimization parameters,
objective function, and volume constraint are presented along with the
function evaluation number where they occurred, total function
evaluation counts, and a timing summary. In the end, the objective
function has been minimized and the equality constraint has been
satisfied (driven to zero within the constraint tolerance).
Majr Minr Step Fun Merit function Norm gZ Violtn nZ Penalty Convfort.9 or ftn09).
