Abstract
A new derivative-free method is proposed for solving equality-constrained nonlinear optimization problems. This method is of the trust-funnel variety and is also based on the use of polynomial interpolation models. In addition, it uses a self-correcting geometry procedure in order to ensure that the interpolation problem is well defined in the sense that the geometry of the set of interpolation points does not differ too much from the ideal one. The algorithm is described in detail and some encouraging numerical results are presented.
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References
Bandeira, A.S., Scheinberg, K., Vicente, L.N.: Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization. Math. Program. 134(1), 223–257 (2012)
Colson, B.: Trust-Region Algorithms for Derivative-Free Optimization and Nonlinear Bilevel Programming. PhD Thesis, Department of Mathematics, FUNDP-University of Namur, Namur, Belgium, (2004)
Conn, A.R., Gould, N.I.M., Toint, PhL: Trust-Region Methods. MOS-SIAM Series on Optimization. SIAM, Philadelphia (2000)
Conn, A.R., Scheinberg, K., Toint, PhL: On the convergence of derivative-free methods for unconstrained optimization. In: Iserles, A., Buhmann, M. (eds.) Approximation Theory and Optimization: Tributes to M.J.D. Powell, pp. 83–108. Cambridge University Press, Cambridge (1997)
Conn, A.R., Scheinberg, K., Toint, Ph.L.: A derivative-free optimization algorithm in practice. In: Proceedings of the 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, St. Louis, MO, (1998)
Conn, A.R., Scheinberg, K., Vincente, L.N.: Introduction to Derivative-Free Optimization. MPS-SIAM Book Series on Optimization. SIAM, Philadelphia (2009)
Conn, A.R., Scheinberg, K., Zang, H.: A derivative-free algorithm for least-squares minimization. SIAM J. Opt. 20(6), 3555–3576 (2010)
Dennis, J.E., Schnabel, R.B., Methods, Numerical, for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Englewood Cliffs, NJ (1983). Reprinted as Classics in Applied Mathematics, vol. 16. SIAM, Philadelphia (1996)
Dolan, E., Moré, J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002)
Fasano, G., Nocedal, J., Morales, J.-L.: On the geometry phase in model-based algorithms for derivative-free optimization. Opt. Methods Softw. 24(1), 145–154 (2009)
Fletcher, R., Leyffer, S.: Nonlinear programming without a penalty function. Math. Program. 91(2), 239–269 (2002)
Gill, P.E., Murray, W., Wright, M.H.: Pract. Opt. Academic Press, London (1981)
Gould, N.I.M., Orban, D., Toint, PhL: CUTEr, a constrained and unconstrained testing environment, revisited. ACM Trans. Math. Softw. 29(4), 373–394 (2003)
Gould, N.I.M., Toint, PhL: Nonlinear programming without a penalty function or a filter. Math. Program. 122(1), 155–196 (2010)
Gould, N.I.M., Hribar, M.E., Nocedal, J.: On the solution of equality constrained quadratic programming problems arising in optimization. SIAM J. Sci. Comput. 23(4), 1376–1395 (2001)
Gratton, S., Toint, PhL, Tröltzsch, A.: An active-set trust-region method for bound-constrained nonlinear optimization without derivatives. Opt. Methods Softw. 26(4–5), 875–896 (2011)
Lewis, R.M., Torczon, V.: Pattern search algorithms for bound constrained minimization. SIAM J. Opt. 9, 1082–1099 (1999)
Lewis, R.M., Torczon, V.: Pattern search algorithms for linearly constrained minimization. SIAM J. Opt. 10, 917–941 (2000)
Lewis, R.M., Torczon, V.: A globally convergent augmented langragian pattern search algorithm for optimization with general constraints and simple bounds. SIAM J. Opt. 12(4), 1075–1089 (2002)
Lewis, R.M., Torczon, V.: Active set identification for linearly constrained minimization without explicit derivatives. SIAM J. Opt. 20(3), 1378–1405 (2009)
Lewis, R.M., Torczon, V., Trosset, M.W.: Direct search methos: then and now. J. Comput. Appl. Math. 124(1–2), 191–207 (2000)
Moré, J.J.: The Levenberg–Marquardt algorithm: implementation and theory. In: Watson, G.A. (ed.) Numerical Analysis. Lecture Notes in Mathematics, chapter, vol. 10. Springer, Berlin (1978)
Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–313 (1965)
Nocedal, J., Wright, S.J.: Numerical Optimization. Series in Operations Research. Springer, Heidelberg (1999)
Omojokun, E.O.: Trust region algorithms for optimization with nonlinear equality and inequality constraints. PhD Thesis, University of Colorado, Boulder, Colorado (1989)
Powell, M.J.D.: A direct search optimization method that models the objective and constraint functions by linear interpolation. In: Advances in Optimization and Numerical Analysis, Proceedings of the Sixth Workshop on Optimization and Numerical Analysis, Oaxaca, Mexico, vol. 275, pp. 51–67. Kluwer Academic Publishers, Dordrecht, The Netherlands (1994)
Powell, M.J.D.: Direct search algorithms for optimization calculations. Acta Numerica 7, 287–336 (1998)
Powell, M.J.D.: UOBYQA: unconstrained optimization by quadratic interpolation. Math. Program. Ser. A 92, 555–582 (2002)
Powell, M.J.D.: Least Frobenius norm updating of quadratic models that satisfy interpolation conditions. Math. Program. Ser. B 100(1), 183–215 (2004)
Powell, M.J.D.: The newuoa software for unconstrained optimization without derivatives. In: Pillo, G., Roma, M., Pardalos, P. (eds.) Large Scale Nonlinear Optimization, Nonconvex Optimization and Its Applications, vol. 83, pp. 255–297. Springer, New York (2006)
Powell, M.J.D.: Developments of NEWUOA for minimization without derivatives. IMA J. Numer. Anal. 28(4), 649–664 (2008)
Powell, M.J.D.: The BOBYQA algorithm for bound constrained optimization without derivatives. Department of Applied Mathematics and Theoretical Physics, Cambridge University, Cambridge, England, Technical report (2009)
Scheinberg, K., Toint, PhL: Self-correcting geometry in model-based algorithms for derivative-free unconstrained optimization. SIAM J. Opt. 20(6), 3512–3532 (2010)
Steihaug, T.: The conjugate gradient method and trust regions in large scale optimization. SIAM J. Numer. Anal. 20(3), 626–637 (1983)
Stewart, G.W.: A modification of davidon’s minimization method to accept difference approximations of derivatives. J. ACM 14(1), 72–83 (1967)
Toint, PhL: Towards an efficient sparsity exploiting Newton method for minimization. In: Duff, I.S. (ed.) Sparse Matrices and Their Uses. pp. 57–88. Academic Press, London (1981)
Tröltzsch, A.: An active-set trust-region method for bound-constrained nonlinear optimization without derivatives applied to noisy aerodynamic design problems. PhD Thesis, University of Toulouse (2011)
Winfield, D.: Function and functional optimization by interpolation in data tables. PhD Thesis, Harvard University, Cambridge (1969)
Winfield, D.: Function minimization by interpolation in a data table. J. Inst. Math. Appl. 12, 339–347 (1973)
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The first author gratefully acknowledges a CERUNA-UNamur scholarship.
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Sampaio, P.R., Toint, P.L. A derivative-free trust-funnel method for equality-constrained nonlinear optimization. Comput Optim Appl 61, 25–49 (2015). https://doi.org/10.1007/s10589-014-9715-3
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DOI: https://doi.org/10.1007/s10589-014-9715-3