EFCOSS: An interactive environment facilitating optimal experimental design

ACM Transactions on Mathematical Software

Volumn 37, Issue 2, 2010, Pages

  Rasch, Arno ^a   Bücker, H Martin ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

Automatic differentiation; Distributed computing; Optimal experimental design; Parameter estimation; Problem solving environments

Indexed keywords

AUTOMATIC DIFFERENTIATIONS; COMPUTATIONAL SCIENCE AND ENGINEERINGS; DESIGN PHILOSOPHY; DISTRIBUTED COMPUTING; ESTIMATION PROBLEM; INTERACTIVE ENVIRONMENTS; INTERACTIVE SOFTWARE; LARGE SCALE SIMULATIONS; NUMERICAL SIMULATION; OBJECTIVE FUNCTIONS; OPTIMAL EXPERIMENTAL DESIGNS; OPTIMIZATION PROBLEMS; OPTIMIZATION SOFTWARE PACKAGE;

COMPUTER SIMULATION; COMPUTER SOFTWARE; DESIGN; METAL ANALYSIS; OPTIMIZATION; PARAMETER ESTIMATION; PHILOSOPHICAL ASPECTS; STATISTICS;

PROBLEM SOLVING;

EID: 77951927092     PISSN: 00983500     EISSN: 15577295     Source Type: Journal    
DOI: 10.1145/1731022.1731023     Document Type: Article

References (59)

1. ABATE, J., BISCHOF, C., CARLE, A., AND ROH, L. 1997. Algorithms and design for a second-order automatic differentiation module. In Proceedings of the International Symposium on Symbolic and Algebraic Computing (ISSAC). ACM Press, New York, 149-155.
2. ANDERSON, E., BAI, Z., BISCHOF, C., BLACKFORD, S., DEMMEL, J., DONGARRA, J., DU CROZ, J., GREENBAUM, A., HAMMARLING, S., MCKENNEY, A., AND SORENSEN, D. 1999. LAPACK Users Guide, third ed. Society for Industrial and Applied Mathematics, Philadelphia, PA.
3. ASCHER, D., DUBOIS, P. F., HINSEN, K., HUGUNIN, J., AND OLIPHANT, T. 2001. Numerical Python-an open source project. Tech. Rep. UCRL-MA-128569, Lawrence Livermore National Laboratory, Livermore, CA, September.
4. ATKINSON, A. C. AND DONEV, A. N. 1992. Optimum Experimental Designs. Oxford Statistical Science Series, vol.8. Oxford University Press, Oxford, UK.
5. B̈ACK, T. 1996. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press USA, New York.
6. BALAY, S., BUSCHELMAN, K., EIJKHOUT, V., GROPP, W. D., KAUSHIK, D., KNEPLEY, M. G., MCINNES, L. C., SMITH, B. F., AND ZHANG, H. 2004. PETSc users manual. Tech. Rep. ANL-95/11-Revision 2.1.5, Argonne National Laboratory, Argonne, IL.
7. BALAY, S., GROPP, W. D., MCINNES, L. C., AND SMITH, B. F. 1997. Efficient management of parallelism in object oriented numerical software libraries. In Modern Software Tools in Scientific Computing, E. Arge, A. M. Bruaset, and H. P. Langtangen, Eds. Birkhäuser Press, Boston, MA, 163-202.
8. BARDOW, A., BISCHOF, C. H., B̈UCKER, H. M., DIETZE, G., KNEER, R., LEEFKEN, A., MARQUARDT, W., RENZ, U., AND SLUSANSCHI, E. 2008. Sensitivity-based analysis of the k-ε model for the turbulent flow between two plates. Chem. Eng. Sci. 63, 19, 4763-4775.
9. BEAZLEY, D. M. 1996. SWIG: An easy to use tool for integrating scripting languages with C and C++. In Proceedings of the 4th Annual USENIX Tcl/Tk Workshop, Monterey. 129-139.
10. BEAZLEY, D. M. 2003. Automated scientific software scripting with SWIG. Future Gen. Comput. Syst. 19, 5, 599-609.
11. BENSON, S. J., MCINNES, L. C., MOŔE, J., AND SARICH, J. 2005. TAO user manual (revision 1.8). Tech. Rep. ANL/MCS-TM-242, Mathematics and Computer Science Division, Argonne National Laboratory. http://www.mcs.anl.gov/tao.
12. BENSON, S. J.,MCINNES, L. C., ANDMOŔE, J. J. 2001. A case study in the performance and scalability of optimization algorithms. ACM Trans. Math. Softw. 27, 3, 361-376.
13. BISCHOF, C., CARLE, A., KHADEMI, P., AND MAUER, A. 1996. Adifor 2.0: Automatic differentiation of Fortran 77 programs. IEEE Computational Science & Engineering 3, 3, 18-32.
14. BISCHOF, C. H., BÜCKER, H. M., LANG, B., AND RASCH, A. 2003a. An interactive environment for supporting the transition from simulation to optimization. Sci. Prog. 11, 4, 263-272.
15. BISCHOF, C. H., BÜCKER, H. M., LANG, B., AND RASCH, A. 2003b. Solving large-scale optimization problems with EFCOSS. Advances Eng. Softw. 34, 10, 633-639.
16. BISCHOF, C. H., BÜCKER, H. M., RASCH, A., AND SLUSANSCHI, E. 2003. Evaluation of a computer model for wavy falling films using EFCOSS. In Computational Science and Its Applications-Proceedings of the International Conference on Computational Science and its Applications (ICCSA) Part II, V. Kumar, M. L. Gavrilova, C. J. K. Tan, and P. LEcuyer, Eds. Lecture Notes in Computer Science, vol.2668. Springer, Berlin, 78-84.
17. BISCHOF, C. H., ROH, L., AND MAUER, A. 1997. ADIC-An extensible automatic differentiation tool for ANSI-C. Softw. Practice Experience 27, 12, 1427-1456.
18. BOUARICHA, A. AND SCHNABEL, R. B. 1997. Algorithm 768: TENSOLVE: A software package for solving systems of nonlinear equations and nonlinear least-squares problems using tensor methods. ACM Trans. Math. Softw. 23, 2, 174-195.
19. CHOW, T.-T., ESKOW, E., AND SCHNABEL, R. B. 1994. Algorithm 739: A software package for unconstrained optimization using tensor methods. ACM Trans. Math. Softw. 20, 4, 518-530.
20. CZYZYK, J., MESNIER, M. P., AND MOŔE, J. J. 1998. The NEOS server. IEEE Computational Science & Engineering 5, 3, 68-75.
21. DENNIS, J. E.,GAY,D.M., ANDWELSCH,R.E. 1981a. An adaptive nonlinear least-squares algorithm. ACM Trans. Math. Softw. 7, 3, 348-368.
22. DENNIS, J. E., GAY, D. M., AND WELSCH, R. E. 1981b. Algorithm 573: NL2SOL-an adaptive nonlinear least-squares algorithm. ACM Trans. Math. Softw. 7, 3, 369-383.
23. DUBOIS, P. F., HINSEN, K., AND HUGUNIN, J. 1996. Numerical Python. Computers in Physics 10, 3, 262-267.
24. DUBOIS, P. F. AND YANG, T.-Y. 1999. Extending Python with Fortran. Comput. Sci. Eng. 1, 5, 66-73.
25. FAGAN, M. AND CARLE, A. 2000. Adifor 3.0 overview. Tech. Rep. CAAM-TR00-03, Rice University, Department of Computational and Applied Mathematics, Houston, TX.
26. FEDOROV, V. V. AND KHABAROV, V. 1986. Duality of optimal designs for model discrimination and parameter estimation. Biometrika 73, 183-190.
27. FERRIS, M. C., MESNIER, M. P., AND MOŔE, J. J. 2000. NEOS and Condor: Solving optimization problems over the Internet. ACM Trans. Math. Softw. 26, 1, 1-18.
28. FORTH, S. A. 2006. An efficient overloaded implementation of forward mode automatic differentiation in MATLAB. ACM Trans. Math. Softw. 32, 2, 195-222.
29. FOX, P. A., HALL, A. P., AND SCHRYER, N. L. 1978. The PORT mathematical subroutine library. ACM Trans. Math. Softw. 4, 2, 104-126.
30. GAY, D. M. 1983. Algorithm 611: Subroutines for unconstrained minimization using a model/trust-region approach. ACM Trans. Math. Softw. 9, 4, 503-524.
31. GAY, D. M. 1990. Usage summary for selected optimization routines. Computing Science Tech. Rep. 153, AT&T Bell Laboratories, Murray Hill, NJ.
32. GILL, P. E., MURRAY, W., AND SAUNDERS, M. A. 2002. SNOPT: An SQP algorithm for large-scale constrained optimization. SIAM J. Optim. 12, 4, 979-1006.
33. GOOD, M., GOUX, J.-P., NOCEDAL, J., AND PEREYRA, V. 2002. iNEOS: An interactive environment for nonlinear optimization. Appl. Numer. Math. 40, 1-2, 49-57.
34. GRISBY, D., LO, S.-L., AND RIDDOCH, D. 2006. The omniORB version 4.1 Users Guide. Apasphere Ltd. and AT&T Laboratories Cambridge.
35. GROPP, W. AND MOŔE, J. J. 1997. Optimization environments and the NEOS server. In Approximation Theory and Optimization, M. D. Buhmann and A. Iserles, Eds. Cambridge University Press, Cambridge, UK, 167-182.
36. GROSS-HARDT, E., SLUSANSCHI, E., BÜCKER, H. M., PFENNIG, A., AND BISCHOF, C. H. 2008. Practical shape optimization of a levitation device for single droplets. Optim. Eng. 9, 2, 179-199.
37. KENNY, J. P., BENSON, S. J., ALEXEEV, Y., SARICH, J., JANSSEN, C. L., MCINNES, L. C., KRISHNAN, M., NIEPLOCHA, J., JURRUS, E., FAHLSTROM, C., ANDWINDUS, T. L. 2004. Component-based integration of chemistry and optimization software. J. Computat. Chem. 25, 14, 1717-1725.
38. KOLDA, T. G., LEWIS, R. M., AND TORCZON, V. 2003. Optimization by direct search: New perspectives on some classical and modern methods. SIAM Rev. 45, 3, 385-482.
39. KOüRKEL, S. 2002. Numerische Methoden f u r optimale Versuchsplanungsprobleme bei nichtlinearen DAE-Modellen. Ph.D. thesis, University of Heidelberg, Germany.
40. LAWRENCE, C. T. AND TITS, A. L. 1996. Nonlinear equality constraints in feasible sequential quadratic programming. Optim. Meth. Softw. 6, 4, 265-282.
41. LINDSTRÖM, P. 1982. A stabilized Gauss-Newton algorithm for unconstrained least squares problems. Tech. Rep. UMINF-102.82, Institute of Information Processing, University of Ume° a, Ume° a, Sweden.
42. LINDSTRÖM, P. 1983. A general purpose algorithm for nonlinear least squares problems with nonlinear constraints. Tech. Rep. UMINF-103.83, Institute of Information Processing, University of Ume° a, Ume° a, Sweden.
43. MOŔE, J. J., GARBOW, B. S., AND HILLSTROM, K. E. 1980. User guide for MINPACK-1. Tech. Rep. ANL-80-174, Argonne National Laboratory, Argonne, IL.
44. MOŔE, J. J., SORENSEN, D. C., GARBOW, B. S., AND HILLSTROM, K. E. 1984. The MINPACK project. In Sources and Development of Mathematical Software,W. R. Cowell, Ed. Prentice-Hall series in Computational Mathematics. Prentice-Hall, Englewood Cliffs, NJ, 88-111.
45. NOCEDAL, J. AND WRIGHT, S. J. 2006. Numerical Optimization 2nd Ed. Springer Series in Operations Research. Springer, New York.
46. OBJECT MANAGEMENT GROUP. 1999. The common object request broker: Architecture and specification. Revision 2.3.1. ftp://ftp.omg.org/pub/docs/ formal/99-10-07.pdf.
47. OBJECT MANAGEMENT GROUP. 2004. Naming service specification. Version 1.3. ftp://ftp.omg.org/pub/docs/formal/04-10-03.pdf.
48. PETERSON, P. 2005. F2PY: Fortran to Python interface generator. http://cens.ioc.ee/projects/f2py2e.
49. RASCH, A. 2007. Efficient computation of derivatives for optimal experimental design. Ph.D. thesis, Department of Computer Science, RWTH Aachen University, Germany.
50. RASCH, A.,BÜCKER,H. M., ANDBARDOW,A. 2009. Software supporting optimal experimental design: A case study of binary diffusion experiments using EFCOSS. Comput. Chem. Eng. 33, 4, 838-849.
51. SCHITTKOWSKI, K. 2002. EASY-FIT: A software system for data fitting in dynamic systems. Structural Multidisc. Optim. 23, 2, 153-169.
52. SCHNABEL, R. B.,KOONTZ, J. E., ANDWEISS, B. E. 1985. A modular system of algorithms for unconstrained minimization. ACM Trans. Math. Softw. 11, 4, 419-440.
53. SPELLUCCI, P. 1998a. A new technique for inconsistent QP problems in the SQP method. Math. Meth. Oper. Res. 47, 3, 355-400.
54. SPELLUCCI, P. 1998b. An SQP method for general nonlinear programs using only equality constrained subproblems. Math. Prog. 82, 3, 413-448.
55. WÄCHTER, A. AND BIEGLER, L. T. 2006. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Prog. 106, 1, 25-57.
56. WALTER, E. AND PRONZATO, L. 1990. Qualitative and quantitative experiment design for phenomenological models-a survey. Automatica 26, 2, 195-213.
57. WALTER, E. AND PRONZATO, L. 1997. Identification of Parametric Models from Experimental Data. Communications and Control Engineering. Springer Verlag, Berlin.
58. ZHOU, J. L., TITS, A. L., AND LAWRENCE, C. T. 1997. Users Guide for FFSQP Version 3.7-A FORTRAN Code for Solving Constrained Nonlinear (Minimax) Optimization Problems. Institute for Systems Research, University of Maryland, College Park, MD.
59. ZHU, C., BYRD, R. H., LU, P., AND NOCEDAL, J. 1997. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization. ACM Trans. Math. Softw. 23, 4, 550-560.