Numerical Optimization

The four authors are leading international specialists in various branches of nonlinear optimization (one of them received the Dantzig Prize). They are working - or have worked - at INRIA, the French National Institute for Research in Computer Science and Control, and they also teach in various universities and "Grandes Écoles". All of them continually collaborate with industry on problems dealing with optimization, in fields such as energy management, geoscience, life sciences, etc.

From the reviews of the second edition: "This volume is a collection of four coordinated monographs on topics in numerical optimization. ... the four sections of the book fit together to provide a broad survey of methods for numerical optimization at an advanced level. ... this book should be of interest to advanced graduate students and researchers working in numerical optimization." (Brian Borchers, MathDL, March, 2007) "More realistic application problems are introduced with the emphasis on outlining a typical modeling process in more detail. ... the presentation of theoretical results on nonsmooth optimization is reorganized and contains now a new subsection with convergence results. The book provides an excellent basis for studying optimization theory and algorithms, especially for nonsmooth optimization. Additional case studies, availability of computer codes, and exercises improve the understanding of numerical algorithms and the practical problem solving process. In summary, the second edition significantly improves the first one." (Klaus Schittkowski, Zentralblatt MATH, Vol. 1108 (10), 2007)

• Unconstrained Problems.- General Introduction.- Basic Methods.- Line-Searches.- Newtonian Methods.- Conjugate Gradient.- Special Methods.- A Case Study: Seismic Reection Tomography.- Nonsmooth Optimization.- to Nonsmooth Optimization.- Some Methods in Nonsmooth Optimization.- Bundle Methods. The Quest for Descent.- Applications of Nonsmooth Optimization.- Computational Exercises.- Newton's Methods in Constrained Optimization.- Background.- Local Methods for Problems with Equality Constraints.- Local Methods for Problems with Equality and InequalityConstraints.- Exact Penalization.- Globalization by Line-Search.- Quasi-Newton Versions.- Interior-Point Algorithms for Linear and QuadraticOptimization.- Linearly Constrained Optimization and SimplexAlgorithm.- Linear Monotone Complementarity and Associated Vector Fields.- Predictor-Corrector Algorithms.- Non-Feasible Algorithms.- Self-Duality.- One-Step Methods.- Complexity of Linear Optimization Problems with Integer Data.- Karmarkar's Algorithm.