https://github.com/jacobwilliams/schittkowski-test-problems
Mirror of Schittkowski's Test Problems for Nonlinear Programming
https://github.com/jacobwilliams/schittkowski-test-problems
constrained-optimization fortran
Last synced: about 2 months ago
JSON representation
Mirror of Schittkowski's Test Problems for Nonlinear Programming
- Host: GitHub
- URL: https://github.com/jacobwilliams/schittkowski-test-problems
- Owner: jacobwilliams
- Created: 2025-03-03T19:58:59.000Z (2 months ago)
- Default Branch: master
- Last Pushed: 2025-03-08T18:33:11.000Z (2 months ago)
- Last Synced: 2025-03-08T19:28:34.612Z (2 months ago)
- Topics: constrained-optimization, fortran
- Language: Fortran
- Homepage: https://klaus-schittkowski.de/tpnp.htm
- Size: 449 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.TXT
Awesome Lists containing this project
README
TEST EXAMPLES FOR CONSTRAINED NONLINEAR PROGRAMMING
---------------------------------------------------
Purpose:
-------
306 test problems of two previous collections
W. Hock, K. Schittkowski, 'Test Examples for Nonlinear
Programming Codes', Lecture Notes in Economics and Mathematical
Systems, Springer, No, 187, 1981
K. Schittkowski, 'More Test Examples for Nonlinear Programming
Codes', Lecture Notes in Economics and Mathematical Systems,
Springer, No, 282, 1987
are provided in form of Fortran source code together with a test frame.
A decision is made which of the runs is successful, and performance
results are evaluated. With the default tolerances given, all problems
can be solved successfully by the code NLPQLP, a new version of the
SQP implementation NLPQL of the author. Results of NLPQLP are
included for comparative studies.
FILES:
-----
1. PROB.FOR: Fortran codes of the test problems of the two collections
mentioned above, which contain full documentation of the usage of
the subroutines. Note that in some cases, gradients are not provided.
IMPORTANT: The author gives no warranty that the the gradients, as
far as included, are correct!
2. CONV.FOR: Interface between the individual test problem codes and
an available optimization routine, to facilitate the calling procedure
and to eb able to execute all test problems within a loop.
3. TESTP.FOR: Test program that executes 306 problems in a loop. The calling
sequence for the the SQP code NLPQLP is included to give an example.
Three different approximation formulae for gradient evaluations are
included, and a loop over randomly generated errors added to the problem
functions is available. The code generates three output files listed below.
4. TEST.DAT: Output file of the test frame containing numerical results
obtained by NLPQLP. Typical contants of TEST.DAT without lines generated
by the NLP routine:
1 2 0 0 0 26 19 178 0.00000000E+00 0.73114619E-10 0.73E-10 0.00E+00
2 2 0 0 0 20 15 140 0.50426188E-01 0.50426193E-01 0.11E-06 0.00E+00
3 2 0 0 0 10 10 90 0.00000000E+00 0.16103740E-19 0.16E-19 0.00E+00
4 2 0 0 0 2 2 18 0.26666667E+01 0.26666667E+01 0.00E+00 0.00E+00
5 2 0 0 0 8 6 56 -0.19132230E+01 -0.19132230E+01 0.11E-10 0.00E+00
...............
The following data are listed, see TESTP.FOR for details:
NTP (Test problem number)
N (Number of variables)
ME (Number of equality constraints)
M (Number of constraints)
IFAIL (Convergence criterion)
NF (Number of objective function evaluations)
NDF (Number of gradient evaluations of objective function)
NEF (Number of equivalent function evaluations, i.e. NF plus
number of function calls needed for gradient approximation)
FEX (Exact objective function value)
F (Cmputed objective function value)
DFX (Relative error in objective function)
DGX (Sum of constraint violations including bound violations)
5. TEST.TEX: Same as above, buyt with Latex separators.
6. RESULT.DAT: The following summary is shown:
Flag for evaluating gradients
Tolerance for gradient approximation
Termination accuracy for NLP routine
Randomly generated error added to objective
Total number of test runs
Number of successful test runs
- constraint violation less than squared tolerance and
either error in objective less than tolerance or
termination criteria of NLP routine satisfied
Number of better test runs
- constraint violation less than squared tolerance and
error in objective less than -tolerance
Number of local solutions obtained
Number of runs without satisfying termination accuracy
- as indicated by NLP routine, i.e. IFAIL>0
Tolerance for determining successful return
Average number of function evaluations
- NLFUNC calls for successful returns
Average number of gradient evaluations
- NLGRAD calls for successful returns
Average number of equivalent function calls
- additionlly counted function calls for gradient
approximations
Total execution time over all test runs (sec)
7. TEMP.DAT: Contains the same data in one row
Compilation and Link:
--------------------
To run the code, the following files must be linked:
TESTP.OBJ : test program
PROB.OBJ : individual test problems
CONV.OBJ : interface for executing individual test problems
NLPQLP.OBJ : NLP solver
QL.OBJ : auxiliary routine required by solver NLPQLP
References:
---------
K. Schittkowski. NLPQL: A Fortran subroutine solving
constrained nonlinear programming problems, Annals of
Operations Research, Vol.5 (1985/6), 485-500.
K. Schittkowski. NLPQLP: A Fortran implementation
of a sequential quadratic programming algorithm with
distributed and non-monotone line search, Report,
Department of Computer Science, University of Bayreuth,
2006
Author (C):
----------
Prof. K. Schittkowski
Dept. of Computer Science
University of Bayreuth
D-95440 Bayreuth
Germany
http:\\www.klaus.schittkowski.de
[email protected]
Bayreuth, April 2008