Decison Tree for Optimization Software (original) (raw)

Tutorials and Books

Tutorials and online books

A list of books

Optimization is a very lively area, hence standard textbooks become outdated very fast. Therefore only a very restricted and certainly subjective list of books is presented here, mainly extracted from the FAQs initiated by Gregory and presently maintained by R. Fourer.

Books on or containing a considerable amount of LP theory or practice:

Bertsimas, D. and Tsitsiklis, J.: Introduction to Linear Optimization. Athena Scientific, 1997.
Graduate-level text on linear programming, network flows, and discrete optimization.

Maros, I., Computational Techniques of the Simplex Method
Recent comprehensive monograph

Dantzig, G. B.: Linear Programming and Extensions, Princeton University Press, 1963
The most widely cited early textbook in the field.

Dantzig, George B. and Thapa, Mukund N.: Linear Programming 1: Introduction, Springer Verlag, 1997

Juenger, M. et al.: Mixed Integer Programming Computation, Springer, 2009
50 Years of Integer Programming 1958-2008

Luenberger, D. G. and Ye, Yinyu: Introduction to Linear and Nonlinear Programming, Springer, 2008
Updated version of an old classic. Well suited for beginners

Nash, S. and Sofer, A.: Linear and Nonlinear Programming, McGraw-Hill, 1996

Roos, C., Terlaky T. and Vial, J. Ph.: Theory and Algorithms for Linear Optimization: An Interior Point Approach. John Wiley, Chichester, 1997

Schrijver, A.: Theory of Linear and Integer Programming, John Wiley, 1999
Advanced, very well written

Vanderbei, R. J.: Linear Programming: Foundations and Extensions. Springer, 1996
Balanced coverage of simplex and interior-point methods. Source code available on-line for all algorithms presented.

Williams, H.P., Model Building in Mathematical Programming, John Wiley 1999, 4th edition
Little on algorithms, but excellent for learning what makes a good model

Wright, St. J.: Primal-Dual Interior-Point Methods. SIAM Publications, 1997
Covers theoretical, practical and computational aspects of the most important and useful class of interior-point algorithms

Ye, Yinyu: Interior Point Algorithms: Theory and Analysis. John Wiley, 1997

Kellerer, H. et al: Knapsack Problems. Springer-Verlag, 2003

Now a table of books mainly devoted to nonlinear programming

Conn, A.R., Scheinberg, K., and Vicente, L.N.: Introduction to Derivative-Free Optimizati. SIAM:2009
First contemporary comprehensive treatment of optimization without derivatives

Avriel, M. and Golany, B.: Mathematical Programming for Industrial Engineers. Marcel Dekker:1996
Contains introductory chapters to several areas of mathematical optimization. well suited for beginners

Bonnans, J.F., Gilbert, J.C., Lemarechal, C., Sagastizabal, C.A.: Numerical Optimization (2nd edition). Springer: 2006
Both theory and details on implementations; nonsmooth optimization, interior-point methods etc.

Bertsekas, Dimitri P.: Nonlinear Programming, second edition. Athena Scientific, 1999

Bjoerck, Ake : Numerical methods for least squares problems. Philadelphia, SIAM 1996
Very well written book with lots of nonstandard information.

Dennis, E. and Schnabel, B.: Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice Hall, 1983. (reprinted by SIAM)
a classic in its field

Dempe, Stephan: Foundations of Bilevel Programming, Springer Verlag, 2002
Bilevel programming, Theory and algorithms.

Du, D.-Z. and Pardalos, P.: Minimax and Applications. Springer, 1995

Fiacco, A. and McCormick, G. P.: Sequential Unconstrained Minimization Techniques. Reprinted by SIAM
A classic from 1968, given new life by the interior point LP methods

Fletcher, R.: Practical Methods of Optimization. John Wiley, 2000.
"The" reference at the date of its printing.

Gill, Ph.E., Murray, W. and Wright, M.: Practical methods of optimization. New York:Acad. Press 1982
a bit dated with respect to methods, but with many hints for practitioners

M. Locatelli and F. Schoen: Global Optimization
Theory, Algorithms, and Applications

Th. Weise: Global Optimization Algorithms - Theory and Application
downloadable

Hansen, E, and G.W. Walster: Global Optimization Using Interval Analysis. Dekker, 2003.
(second edition)

Horst R., Pardalos P., and Thoai, V.: Introduction to global optimization. Springer, 1995.

Horst R. and Pardalos P.: Handbook of Global Optimization. Springer, 1994.

Kelley, C.T.: Iterative methods of optimization. Philadelphia: SIAM 1999

Kelley, C.T.: Iterative methods for Linear and Nonlinear Equations. Philadelphia: SIAM 1995

Miettinen,K.: Nonlinear Multiobjective Optimization, Springer. 1999

More, J.J. and Wright, St.: Optimization Software Guide. SIAM, 1993.
Contains overview and comments existing software, mainly commercial.

Nemhauser, G.L.: Optimization. (Handbook in Operations Research and Management Science Vol I). North Holland, 1989.
Contains excellent introductions to severals areas of optimization.

Nocedal, J. and Wright, St.: Numerical Optimization, 2nd ed.. Springer Verlag, 2006.
Very well written modern introduction into continuous optimization.

Pinter, J.D.: Global Optimization in Action. Springer, 1996.
Book received 2000 INFORMS Computing Society Prize

Pinter, J.D.: Computational Global Optimization in Nonlinear Systems. Lionheart Publ., 2001.
short e-book, demo software included

Pinter, J.D.: Global Optimization with Maple, Maplesoft.
interactive e-book

Spellucci, P.: Numerische Verfahren der nichtlinearen Optimierung. Birkhäuser, Basel 1993 (in German)

Sergeyev, Y. D., Strongin, R. G. and Lera, D.: Introduction to Global Optimization Exploiting Space-Filling Curves, Springer 2013

Alexander Forrester, András Sobester, Andy Kean: Engineering Design via Surrogate Modelling: A Practical Guide, Wiley 2008

Tawarmalani, M. and Sahinidis, N. V.: Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming. Springer, 2002.
Based on BARON software.

Books on computational/automatic differentiation

Corliss, G. et al (eds.): Automatic Differentiation of Algorithms: From Simulation to Optimization
Springer 2002; Survey chapter, extensive applications chapters, and bibliography

Bücker, M. et al (eds.): Automatic Differentiation - Applications, Theory and Implementations
Springer 2006; covers the state of the art in automatic differentiation theory and practice

Griewank, A. and Walther, A. : Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition
SIAM 2008

Non-classical techniques and constraint programming

Special Issue on the Next 10 Years of Constraint Programming, downloadable (2007),
Intro and five articles

Roberto Battiti, Mauro Brunato and Franco Mascia: Reactive Search and Intelligent Optimization, downloadable (2007),
integrates machine learning techniques

Dorigo, M., and Stützle, Th.: Ant Colony Optimization,
MIT Press, 2004: comprehensive coverage of this metaheuristic including software

Corne D., Dorigo, M., and Glover, F.: New Ideas in Optimization:
Chapters on various methods: Simulated Annealing, Genetic Programming, Tabu Search, Differential Evolution etc

Michalewicz, Z., Fogel, D.B.: How to Solve It: Modern Heuristics,
Springer Verlag 2000

Siarry, P. and Michalewicz, Z.: Advances in Metaheuristics for Hard Optimization,
Springer Verlag 2007