Titlebook: Introduction to Nonsmooth Optimization; Theory, Practice and Adil Bagirov,Napsu Karmitsa,Marko M. Mäkelä Book 2014 Springer International P

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Convex Analysis geometrical viewpoint by examining the tangent and normal cones of convex sets. Then we generalize the concepts of differential calculus for convex, not necessarily differentiable functions. We define subgradients and subdifferentials and present some basic results. At the end of this chapter, we l

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Generalized Convexities has been the most important concept since the basic reference by Rockafellar was published. Different types of generalized convexities have proved to be the main tool when constructing optimality conditions, particularly sufficient conditions for optimality. In this chapter, we analyze the properti

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SemiAcademic Problemsaller in dimension or simpler in structure. The examples of this kind of methodological nonsmoothness are Lagrange relaxation, different decompositions, dual formulations, and exact penalty functions. In this chapter, we briefly describe some of these formulations. In addition, we represent the maxi

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Academic Problemsion which can be and have been used to test the nonsmooth optimization software. We use a classification of test problems such that it is easy to pick up those problems that share features in interest. That is, for instance, convexity or nonconvexity, or min-max, piecewise quadratic or general polyn

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