Abstract
The Douglas–Rachford reflection method is a general-purpose algorithm useful for solving the feasibility problem of finding a point in the intersection of finitely many sets. In this chapter, we demonstrate that applied to a specific problem, the method can benefit from heuristics specific to said problem which exploit its special structure. In particular, we focus on the problem of protein conformation determination formulated within the framework of matrix completion, as was considered in a recent paper of the present authors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
When two amino acids form a peptide bond, a water molecule is formed. An amino acid residue is what remains of each amino acid after this reaction.
- 2.
RCSB Protein Data Bank: www.rcsb.org/pdb.
References
Aragón Artacho, F., Borwein, J.: Global convergence of a non-convex Douglas-Rachford iteration. J. Glob. Optim. 57(3), 753–769 (2013)
Aragón Artacho, F., Borwein, J., Tam, M.: Recent results on Douglas–Rachford methods for combinatorial optimization problems. J. Optim. Theory Appl. (in press, 2013)
Aragón Artacho, F., Borwein, J., Tam, M.: Douglas-Rachford feasibility methods for matrix completion problems. ANZIAM J. 55(4), 299–326 (2014)
Bauschke, H., Bello Cruz, J., Nghia, T., Phan, H., Wang, X.: The rate of linear convergence of the Douglas-Rachford algorithm for subspaces is the cosine of the Friedrichs angle. J. Approx. Theory 185, 63–79 (2014)
Bauschke, H., Combettes, P.: Convex analysis and monotone operator theory in Hilbert space. Springer, New York (2011)
Bauschke, H., Combettes, P., Luke, D.: Finding best approximation pairs relative to two closed convex sets in Hilbert spaces. J. Approx. Theory 127(2), 178–192 (2004)
Bauschke, H., Noll, D., Phan, H.: Linear and strong convergence of algorithms involving averaged nonexpansive operators. J. Math. Anal. Appl. 421(1), 1–20 (2015)
Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucleic Acids Res. 28, 235–242 (2000)
Bondi, A.: Van der Waals Volumes and Radii. J. Phys. Chem. 68(3), 441–51 (1964)
Borwein, J., Lewis, A.: Convex analysis and nonlinear optimization. Springer (2006)
Borwein, J., Sims, B.: The Douglas–Rachford algorithm in the absence of convexity. In: Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp. 93–109. Springer (2011)
Borwein, J., Tam, M.: The cyclic Douglas-Rachford method for inconsistent feasibility problems. J. Nonlinear Convex Anal. 16(4), 537–584 (2015)
Borwein, J., Tam, M.: A cyclic Douglas-Rachford iteration scheme. J. Optim. Theory Appl. 160(1), 1–29 (2014)
Borwein, J., Zhu, Q.: Techniques of Variational Analysis, CMS Books in Mathematics, vol. 20. Springer-Verlag, New York (2005, Paperback, 2010)
Berman, A., Shaked-Monderer, N.: Completely positive matrices. World Scientific, Singapore (2003)
Cegielski, A.: Iterative methods for fixed point problems in Hilbert space. Lecture Notes in Mathematics, vol. 2057. Springer, London (2012)
Dattorro, J.: Convex optimization & Euclidean distance geometry. Meboo Publishing USA (2005)
Elser, V., Rankenburg, I., Thibault, P.: Searching with iterated maps. Proc. Natl. Acad. Sci. 104(2), 418–423 (2007)
Gravel, S., Elser, V.: Divide and concur: A general approach to constraint satisfaction. Phys. Rev. E 78(3), 036,706 (2008)
Hayden, T., Wells, J.: Approximation by matrices positive semidefinite on a subspace. Linear Algebra Appl. 109, 115–130 (1988)
Hesse, R., Luke, D.: Nonconvex notions of regularity and convergence of fundamental algorithms for feasibility problems. SIAM J. Optim. 23(4), 2397–2419 (2013)
Seo, J., Kim, J.-K., Ryu, J., Lavor, C., Mucherino, A., Kim, D.-S.: BetaMDGP: Protein structure determination algorithm based on the Beta-complex. Trans. Comput. Sc. 8360, 130–155 (2014)
Acknowledgements
The authors wish to thank Dr. Alister Page for introducing us to the bulk structure determination problem and for kindly sharing the PAN data set. The work of JMB is supported in part by the Australian Research Council. This work was performed during MKT’s candidature at the University of Newcastle where he was supported in part by an Australian Postgraduate Award.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Borwein, J.M., Tam, M.K. (2017). Reflection Methods for Inverse Problems with Applications to Protein Conformation Determination. In: Aussel, D., Lalitha, C. (eds) Generalized Nash Equilibrium Problems, Bilevel Programming and MPEC. Forum for Interdisciplinary Mathematics. Springer, Singapore. https://doi.org/10.1007/978-981-10-4774-9_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-4774-9_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4773-2
Online ISBN: 978-981-10-4774-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)