Titlebook: Numerical Nonsmooth Optimization; State of the Art Alg Adil M. Bagirov,Manlio Gaudioso,Sona Taheri Book 2020 Springer Nature Switzerland AG

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Bundle Methods for Nonsmooth DC Optimizationconditions are discussed and the relationship between sets of different stationary points (critical, Clarke stationary and inf-stationary) is established. Bundle methods are developed based on a nonconvex piecewise linear model of the objective function and the convergence of these methods is studie

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Beyond the Oracle: Opportunities of Piecewise Differentiationoracle that evaluates at any given . the objective function value .(.) and a generalized gradient .?∈?.(.) in the sense of Clarke. We will argue here that, if there is a realistic possibility of computing a vector . that is guaranteed to be a generalized gradient, then one must know so much about th

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Numerical Solution of Generalized Minimax Problemssts in the minimization of nonsmooth functions which are compositions of special smooth convex functions with maxima of smooth functions. The most important functions of this type are the sums of maxima of smooth functions. Section 11.2 is devoted to primal interior point methods which use solutions

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New Multiobjective Proximal Bundle Method with Scaled Improvement Functioncase the improvement function possesses, for example the nice property that a descent direction for the improvement function improves all the objectives of the original problem. However, the numerical experiments have shown that the standard improvement function is rather sensitive for scaling. For

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