# CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization

- 786 Downloads
- 48 Citations

## Abstract

We describe the most recent evolution of our constrained and unconstrained testing environment and its accompanying SIF decoder. Code-named SIFDecode and CUTEst, these updated versions feature dynamic memory allocation, a modern thread-safe Fortran modular design, a new Matlab interface and a revised installation procedure integrated with GALAHAD.

## Keywords

CUTE CUTEr CUTEst Optimization Modeling Benchmarking## Notes

### Acknowledgments

We are extremely grateful to Roger Fletcher, Philip Gill, Bill Hager, Michal Kočvara, Michael Powell, Klaus Schittkowski and Elizabeth Wong for making their latest codes available to us so that we could build and test interfaces, and to two anonymous referees whose enthusiastic comments lead to a better paper. The work of the first author was supported by the EPSRC Grant EP/I013067/1. The work of the second author was supported by an NSERC Discovery Grant.

## References

- 1.Birgin, E.G., Martinez, J.M., Raydan, M.: Algorithm 813: SPG–software for convex-constrained optimization. ACM Trans. Math. Softw.
**27**, 340–349 (2001)CrossRefMATHGoogle Scholar - 2.Birgin, E.G., Castillo, R., Martinez, J.M.: Numerical comparison of augmented Lagrangian algorithms for nonconvex problems. Comput. Optim. Appl.
**31**(1), 31–56 (2005)CrossRefMATHMathSciNetGoogle Scholar - 3.Bongartz, I., Conn, A.R., Gould, N.I.M., Toint, Ph.L.: \({\sf CUTE}\): constrained and unconstrained testing environment. ACM Trans. Math. Softw.
**21**(1), 123–160 (1995)Google Scholar - 4.Conn, A.R., Gould, N.I.M., Toint, Ph.L.: An introduction to the structure of large scale nonlinear optimization problems and the \({\sf LANCELOT}\) project. In: Glowinski, R., Lichnewsky, A. (eds.) Computing Methods in Applied Sciences and Engineering, pp. 42–51. SIAM, Philadelphia (1990)Google Scholar
- 5.Conn, A.R., Gould, N.I.M., Toint, Ph.L.: \({\sf LANCELOT}\): A Fortran Package for Large-Scale Nonlinear Optimization (Release A). Springer Series in Computational Mathematics. Springer, Heidelberg, Berlin, New York (1992)Google Scholar
- 6.Le Digabel, S.: Algorithm 909: NOMAD: nonlinear optimization with the MADS algorithm. ACM Trans. Math. Softw.
**37**(4), 1–15 (2011)CrossRefGoogle Scholar - 7.Dolan, E.D., Gurson, A.P., Shepherd, P.L., Siefert, C.M., Torczon, V.J., Yates, A.: C++ direct searches. http://www.cs.wm.edu/va/software/DirectSearch/direct_code/ (2001)
- 8.Fletcher, R.: A sequential linear constraint programming algorithm for NLP. SIAM J. Optim.
**22**(3), 772–794 (2012)CrossRefMATHMathSciNetGoogle Scholar - 9.Gay, D.M.: Electronic mail distribution of linear programming test problems. Mathematical Programming Society COAL Newsletter, December (1985). http://www.netlib.org/lp/data/
- 10.Gill, P.E., Wong, E.: Methods for convex and general quadratic programming. Technical Report NA 10–1, Department of Mathematics, University of California, San Diego, 2013. To appear Mathematical Programming Computation (2014)Google Scholar
- 11.Gould, N.I.M., Orban, D., Toint, Ph.L.: \({\sf CUTEr}\) (and \({\sf SifDec}\)), a constrained and unconstrained testing environment, revisited. ACM Trans. Math. Softw.
**29**(4), 373–394 (2003)Google Scholar - 12.Gould, N.I.M., Orban, D., Toint, Ph.L.: \({\sf GALAHAD}\)—a library of thread-safe fortran 90 packages for large-scale nonlinear optimization. ACM Trans. Math. Softw.
**29**(4), 353–372 (2003)Google Scholar - 13.IBM Optimization Solutions and Library: QP Solutions User Guide. IBM Corportation (1998)Google Scholar
- 14.International Business Machine Corporation: Mathematical programming system/360 version 2, linear and separable programming-user’s manual. Technical Report H20–0476-2, IBM Corporation, 1969. MPS StandardGoogle Scholar
- 15.Kocvara, M., Stingl, M.: PENNON: a code for convex nonlinear and semidefinite programming. Optim. Methods Softw.
**18**(3), 317–333 (2003)CrossRefMATHMathSciNetGoogle Scholar - 16.Maros, I., Meszaros, C.: A repository of convex quadratic programming problems. Optim. Methods Softw.
**11–12**, 671–681 (1999)CrossRefMathSciNetGoogle Scholar - 17.Ponceleón, D.B.: Barrier methods for large-scale quadratic programming. Ph.D. Thesis, Department of Computer Science, Stanford University, Stanford, CA, USA (1990)Google Scholar
- 18.Powell, M.J.D.: The BOBYQA algorithm for bound constrained optimization without derivatives. Technical Report DAMTP NA2009/06, Department of Applied Mathematics and Theoretical Physics, Cambridge University, Cambridge, UK (2009)Google Scholar
- 19.Powell, M.J.D.: The LINUOA software for linearly unconstrained optimization without derivatives. http://www.netlib.org/na-digest-html/13/v13n42.html (2013)
- 20.Powell, M.J.D.: The NEWUOA software for unconstrained optimization without derivatives. In: Di Pillo, G., Roma, M. (eds.) Large-Scale Nonlinear Optimization. Nonconvex Optimization and Its Applications, vol. 83, pp. 255–297. Springer, Heidelberg, Berlin, New York (2006)CrossRefGoogle Scholar
- 21.QPLIB2014: a Quadratic Programming Library. http://www.lamsade.dauphine.fr/QPlib2014/doku.php (2014)
- 22.Schittkowski, K.: NLPQLP: a Fortran implementation of a sequential quadratic programming algorithm with distributed and non-monotone line search. University of Bayreuth, Department of Computer Science, Technical report (2010)Google Scholar
- 23.Schittkowski, K.: QL: a Fortran code for convex quadratic programming–User’s guide, Version 2.11. Technical report, University of Bayreuth, Department of Computer Science (2005)Google Scholar