Optimization of algorithms with OPAL

Mathematical Programming Computation

Volumn 6, Issue 3, 2014, Pages 233-254

  Audet, Charles ^a   Dang, Kien Cong ^a   Orban, Dominique ^a  

^a GERAD   (Canada)

Author keywords

Autotuning; Nonsmooth optimization; Parameter optimization

Indexed keywords

OPTIMIZATION; PROBLEM SOLVING;

AUTOTUNING; BLACK-BOX OPTIMIZATION; GENERAL-PURPOSE SYSTEMS; MULTIPLE PROCESSORS; NONSMOOTH OPTIMIZATION; PARAMETER OPTIMIZATION; PERFORMANCE MEASURE; SOLVING ALGORITHM;

ALGORITHMS;

EID: 84961292751     PISSN: 18672949     EISSN: 18672957     Source Type: Journal    
DOI: 10.1007/s12532-014-0067-x     Document Type: Article

References (40)

1. Abramson, M.A., Audet, C.: Convergence of mesh adaptive direct search to second-order stationary points. SIAM J. Optimization 17(2), 606–619 (2006). doi:10.1137/050638382
2. Abramson, M.A., Audet, C., Dennis, J.E Jr.: Filter pattern search algorithms for mixed variable constrained optimization problems. Pac. J. Optimization 3(3), 477–500 (2007)
3. Ansel, J., Chan, C., Wong, Y.L., Olszewski, M., Zhao, Q., Edelman, A., Amarasinghe, S.: Petabricks: A Language and Compiler for Algorithmic Choice. In: ACM SIGPLAN Conference on Programming Language Design and Implementation. Dublin, Ireland (2009). doi:10.1145/1542476.1542481
4. Audet, C., Béchard, V., Le Digabel, S.: Nonsmooth optimization through mesh adaptive direct search and variable neighborhood search. J. Glob. Optimization 41(2), 299–318 (2008). doi:10.1007/s10898-007-9234-1
5. Audet, C., Dang, C.K., Orban, D.: Algorithmic parameter optimization of the DFO method with the OPAL framework. In: Naono, J.C.K., Teranishi, K., Suda, R. (eds.) Software Automatic Tuning: From Concepts to State-of-the-Art Results, pp. 255–274. Springer, New York (2010). doi:10.1007/978-1-4419-6935-4_15
6. Audet, C., Dang, C.K., Orban, D.: Efficient use of parallelism in algorithmic parameter optimization applications. Optimization Lett. 7(3), 421–433 (2013). doi:10.1007/s11590-011-0428-6
7. Audet, C., Dennis, J. Jr.: Le Digabel, S.: Trade-off studies in blackbox optimization. Optimization Methods Softw. 27(4–5), 613–624 (2012). doi:10.1080/10556788.2011.571687
8. Audet, C., Dennis, J.E Jr.: Analysis of generalized pattern searches. SIAM J. Optimization 13(3), 889–903 (2003). doi:10.1137/S1052623400378742
9. Audet, C., Dennis, J.E Jr.: Mesh adaptive direct search algorithms for constrained optimization. SIAM J. Optimization 17(1), 188–217 (2006). doi:10.1137/040603371
10. Audet, C., Dennis, J.E Jr.: A progressive barrier for derivative-free nonlinear programming. SIAM J. Optimization 20(1), 445–472 (2009). doi:10.1137/070692662
11. Audet, C., Dennis, J.E Jr.: Digabel, S.L.: Globalization strategies for mesh adaptive direct search. Comput. Optimization Appl. 46(2), 193–215 (2010). doi:10.1007/s10589-009-9266-1
12. Audet, C., Orban, D.: Finding optimal algorithmic parameters using derivative-free optimization. SIAM J. Optimization 17(3), 642–664 (2006). doi:10.1137/040620886
13. Audet, C., Savard, G., Zghal, W.: Multiobjective optimization through a series of single-objective formulations. SIAM J. Optimization 19(1), 188–210 (2008). doi:10.1137/060677513
14. Bilmes, J., Asanović, K., Chin, C.W., Demmel, J.: The PHiPAC v1.0 matrix-multiply distribution. Technical Report TR-98-35, International Computer Science Institute, CS Division, University of California, Berkeley (1998)
15. Booker, A.J., Dennis, J.E Jr.: Frank, P.D., Serafini, D.B., Torczon, V., Trosset, M.W.: A rigorous framework for optimization of expensive functions by surrogates. Struct. Optimization 17(1), 1–13 (1999). doi:10.1007/BF01197708
16. Box, G.E.P.: Evolutionary operation: a method for increasing industrial productivity. Appl. Stat. 6(2), 81–101 (1957)
17. Clarke, F.H.: Optimization and Nonsmooth Analysis. Reissued in 1990 by SIAM Publications, Philadelphia, as, Vol. 5 in the series Classics in Applied Mathematics. Wiley, New York (1983)
18. Conn, A.R., Le Digabel, S.: Use of quadratic models with mesh-adaptive direct search for constrained black box optimization. Optimization Methods Softw. 28(1), 139–158 (2013). doi:10.1080/10556788.2011.623162
19. Conn, A.R., Scheinberg, K., Vicente, L.N.: Introduction to Derivative-Free Optimization. MPS/SIAM Book Series on Optimization. SIAM, Philadelphia (2009)
20. Dang, C.K.: Optimization of algorithms with the OPAL framework. Ph.D. Thesis, École Polytechnique de Montréal, Montréal, Québec, Canada (2012)
21. Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002). doi:10.1007/s101070100263
22. Fermi, E., Metropolis, N.: Numerical solution of a minimum problem. Los Alamos Unclassified Report LA-1492. Los Alamos National Laboratory, Los Alamos (1952)
23. Fletcher, R., Leyffer, S.: Nonlinear programming without a penalty function. Math. Program. Ser. A 91, 239–269 (2002). doi:10.1007/s101070100244
24. Gould, N., Orban, D., Toint, P.: CUTEr (and SifDec): a constrained and unconstrained testing environment, revisited. ACM Trans. Math. Softw. 29(4), 373–394 (2003). doi:10.1145/962437.962439
25. Gould, N.I.M., Orban, D., Sartenaer, A., Toint, P.L.: Sensitivity of trust-region algorithms on their parameters. 40R 3(3), 227–241 (2005). doi:10.1007/s10288-005-0065-y
26. Gramacy, R.B., Le Digabel, S.: The Mesh Adaptive Direct Search Algorithm with Treed Gaussian Process Surrogates. Tech. Rep. G-2011-37, Les cahiers du GERAD (2011)
27. Hooke, R., Jeeves, T.A.: Direct search solution of numerical and statistical problems. J. Assoc. Comput. Mach. 8(2), 212–229 (1961). doi:10.1145/321062.321069
28. Kohonen, T.: The self-organizing map. Neurocomputing 21(1–3), 1–6 (1998). doi:10.1016/S0925-2312(98)00030-7
29. Kohonen, T., Somervuo, P.: How to make large self-organizing maps for nonvectorial data. Neural Netw. 15(8–9), 945–952 (2002). doi:10.1016/S0893-6080(02)00069-2
30. Kokkolaras, M., Audet, C., Dennis, J.E Jr.: Mixed variable optimization of the number and composition of heat intercepts in a thermal insulation system. Optimization Eng. 2(1), 5–29 (2001). doi:10.1023/A:1011860702585
31. Le Digabel, S.: Algorithm 909: NOMAD: nonlinear optimization with the MADS algorithm. ACM Trans. Math. Softw. 37(4), 44:1–44:15 (2011). doi:10.1145/1916461.1916468
32. Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997). doi:10.1016/S0305-0548(97)00031-2
33. Orban, D.: Templating and automatic code generation for performance with python. Tech. Rep. G-2011-30, Les cahiers du GERAD (2011)
34. Pantazi, S., Kagolovsky, Y., Moehr, J.R.: Cluster analysis of wisconsin breast cancer dataset using self-organizing maps. In: Surjàn, G., Engelbrecht, R., Mcnair, P. (eds.) ealth Data in the Information Society, no. 90 in Technology and Informatics. International Congress on Medical Informatics, pp. 431–436. IOS Press, Amsterdam (2002)
35. Seymour, K., You, H., Dongarra, J.J.: A comparison of search heuristics for empirical code optimization. In: Proceedings of the 2008 IEEE International Conference on Cluster Computing, Third international Workshop on Automatic Performance Tuning (iWAPT 2008), pp. 421–429. Tsukuba International Congress Center, EPOCHAL TSUKUBA, Japan (2008). doi: 10.1109/CLUSTR.2008.4663803
36. Torczon, V.: On the convergence of pattern search algorithms. SIAM J. Optimization 7(1), 1–25 (1997). doi:10.1137/S1052623493250780
37. Vicente, L.N., Custódio, A.L.: Analysis of direct searches for discontinuous functions. Math. Program. 133(1–2), 299–325 (2012). doi:10.1007/s10107-010-0429-8
38. Vuduc, R., Demmel, J.W., Yelick, K.A.: OSKI: a library of automatically tuned sparse matrix kernels. In: Proceedings of SciDAC 2005, Journal of Physics: Conference Series. Institute of Physics Publishing, San Francisco (2005). doi:10.1088/1742-6596/16/1/071
39. Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Program. 106(1), 25–57 (2006). doi:10.1007/s10107-004-0559-y
40. Whaley, R.C., Petitet, A., Dongarra, J.J.: Automated empirical optimization of software and the ATLAS project. Parallel Comput. 27(1–2), 3–35 (2001). doi:10.1016/S0167-8191(00)00087-9