Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Biological Sequences Read chapter Read first chapter

2013 | OriginalPaper | Chapter

3. Mathematical Optimization

Authors : Elisa Pappalardo, Panos M. Pardalos, Giovanni Stracquadanio

Published in: Optimization Approaches for Solving String Selection Problems

Publisher: Springer New York

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Mathematical Optimization is an interdisciplinary branch of applied mathematics, related to the fields of Operations Research, Computational Complexity, and Algorithm Theory. It can be informally defined as the science of finding the “best” solution from a set of available alternatives, where the notion of “best” is intrinsically related to the specific problem addressed. Nowadays, optimization problems arise in all sorts of areas and are countless in everyday life, such as in engineering, microelectronics, telecommunications, biomedicine, genetics, proteomics, economics, finance, and physics. However, in spite of the proliferation of optimization algorithms, there is no universal method suitable for all optimization problems, and the choice of the “most appropriate method” to solve the specific problem is demanded to the user. With this in mind, in this chapter we address some general questions about optimization problems and their solutions, in order to provide a background knowledge of the issues analyzed later.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Strings: Theory, Properties and Applications
next chapter String Selection Problems
Literature
1.
Androulakis, I., Maranas, C., Floudas, C.: αbb: A global optimization method for general constrained nonconvex problems. J. Glob. Optim. 7(4), 337–363 (1995)
2.
Barnhart, C., Johnson, E.L., Nemhauser, G.L., Savelsbergh, M.W., Vance, P.H.: Branch-and-price: column generation for solving huge integer programs. Oper. Res. 46(3), 316–329 (1998)MathSciNetCrossRefMATH
3.
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRefMATH
4.
Dantzig, G.B.: Linear Programming and Extensions. Princeton University Press, Princeton (1998)MATH
5.
Dorigo, M., Stützle, T.: The ant colony optimization metaheuristic: Algorithms, applications, and advances. In: Handbook of Metaheuristics, pp. 250–285. Springer, New York (2003)
6.
Du, D.Z., Pardalos, P.M.: Handbook of Combinatorial Optimization, vol. 3. Springer, New York (1998)
7.
8.
Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory of NP-Completeness. WH Freeman, San Francisco (1990)
9.
10.
Glover, F., Laguna, M.: Tabu Search. Wiley, London (1993)
11.
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)MATH
12.
Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11(1), 1–18 (2003)CrossRef
13.
Horst, R., Pardalos, P.M., Van Thoai, N.: Introduction to Global Optimization. Kluwer, Dordrecht (2000)
14.
Ingber, L., Petraglia, A., Petraglia, M.R., Machado, M.A.S., et al.: Adaptive simulated annealing. In: Stochastic Global Optimization and Its Applications with Fuzzy Adaptive Simulated Annealing, pp. 33–62. Springer, New York (2012)
15.
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE, New York (1995)
16.
17.
Kuhn, H.W., Tucker, A.W.: Nonlinear programming. In: Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, vol. 5 (1951)
18.
Michalewicz, Z.: Evolution strategies and other methods. In: Genetic Algorithms + Data Structures = Evolution Programs, pp. 159–177. Springer, New York (1996)
19.
Mitchell, J.E.: Branch-and-cut algorithms for combinatorial optimization problems. In: Handbook of Applied Optimization, pp. 65–77. Oxford, GB: Oxford University Press (2002)
20.
Mitchell, J.E., Pardalos, P.M., Resende, M.G.: Interior point methods for combinatorial optimization. In: Handbook of Combinatorial Optimization, vol. 1, pp. 189–297. Kluwer Academic Publishers (1998)
21.
22.
Nesterov, Y., Nemirovskii, A.S., Ye, Y.: Interior-point Polynomial Algorithms in Convex Programming, vol. 13. Studies in Applied Mathematics, Philadelphia (1994)CrossRefMATH
23.
Neumaier, A.: Complete search in continuous global optimization and constraint satisfaction. Acta Numer. 13(1), 271–369 (2004)MathSciNet
24.
Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Dover, Mineola (1998)MATH
25.
Pardalos, P.M., Resende, M.G.: Interior point methods for global optimization. In: Interior Point Methods of Mathematical Programming. Citeseer (1996)
26.
Pardalos, P.M., Resende, M.G.: Handbook of Applied Optimization, vol. 1. Oxford University Press, Oxford (2002)CrossRefMATH
27.
Pardalos, P.M., Rosen, J.B.: Constrained Global Optimization: Algorithms and Applications. Springer, New York (1987)CrossRefMATH
28.
Pelikan, M.: Bayesian optimization algorithm. In: Hierarchical Bayesian Optimization Algorithm, pp. 31–48. Springer, New York (2005)
29.
Puchinger, J., Raidl, G.: Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification. In: Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, pp. 113–124 (2005)
30.
Schrijver, A.: Theory of Linear and Integer Programming. Wiley, London (1998)MATH
31.
Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefMATH
Metadata
Title
Mathematical Optimization
Authors
Elisa Pappalardo
Panos M. Pardalos
Giovanni Stracquadanio
Copyright Year
2013
Publisher
Springer New York
Book
Optimization Approaches for Solving String Selection Problems

Print ISBN: 978-1-4614-9052-4

Electronic ISBN: 978-1-4614-9053-1

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4614-9053-1_3

Premium Partner

    Image Credits