Optimization theory

Roubicek T. (1997) Relaxation in optimization theory and variational calculus. W de Gruyter, Berlin. 

Beveridge, G. S. G. and Schechter, R. (1970). Optimization Theory and Practice. McGraw Hill, New York. 

The Neyman-Pearson lemma is a very useful result in optimization theory. Here we give without proof a statement of this lemma. 

Schwartz, A., Polak, E., 1997, Family of projected descent methods for optimization problems with simple bounds. Journal of Optimization Theory and Applications 92, 1... 

Rao, S. S., Engineering Optimization Theory and Practice, 3rd ed., John Wiley Sons, New York, 1996. 

The preceding set of characteristics and properties of the estimators makes our type of mapping procedures, /, particularly appealing for the kinds of systems that we are especially interested to study, i.e., manufacturing systems where considerable amounts of data records are available, with poorly understood behavior, and for which neither accurate first-principles quantitative models exist nor adequate functional form choices for empirical models can be made a priori. In other situations and application contexts that are substantially different from the above, while much can still be gained by adopting the same problem statements, solution formats and performance criteria, other mapping and search procedures (statistical, optimization theory) may be more efficient. 

Lasdon, L., Optimization Theory for Large Systems. Macmillan, New York, 1970. 

Minimization of S(k) can be accomplished by using almost any technique available from optimization theory. Next we shall present the Gauss-Newton method as we have found it to be overall the best one (Bard, 1970). 

Floudas CA (2000) Deterministic Global Optimization Theory, Methods and Applications, Kluwer Academic Publishers. 

Steur, R. E. Multiple Criteria Optimization Theory Computation and Application. Wiley, New York (1998). 

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